Citation:

Niravkumar Prajapati, 2025. "The Role of Machine Learning in Big Data Analytics: Tools, Techniques, and Applications", ESP Journal of Engineering & Technology Advancements  5(2): 17-23.

Abstract:

Machine learning (ML) has shown to be a game-changer in big data analytics, allowing businesses to glean valuable insights from massive and intricate datasets. Highlighting the methods, tools, and applications that drive innovation and decision-making across sectors, this review article investigates the complementary nature regarding artificial intelligence and large analytics. Exploring the three cornerstones of under supervision, under supervision reinforcement education, and machine learning, and how each tackles issue like choosing characteristics, prioritization of data, and system adaptability. The paper reviews popular tools and frameworks, including TensorFlow, PyTorch, Hadoop, and Spark, supporting big data analytics. Furthermore, real-world applications in healthcare, finance, e-commerce, and smart cities are examined, showcasing how machine learning (ML) optimizes processes, predicts trends, and enhances user experiences. By identifying emerging trends and future research directions to really reap the rewards of enormous amounts of data, this study offers a thorough analysis of how machine learning contributes.

References:

[1] L. Wang and C. Alexander, “Machine Learning in Big Data,” Int. J. Math. Eng. Manag. Sci., vol. 1, pp. 52–61, 2016, doi: 10.33889/IJMEMS.2016.1.2-006.

[2] N. Das, S. Sagnika, and B. S. Prasad Mishra, “7 Role of Machine Learning in Big Data,” in Applications of Machine Learning in Big-Data Analytics and Cloud Computing, 2021, pp. 139–163.

[3] M. M. Srinivas Murri, Manoj Bhoyar, Guru Prasad Selvarajan, “Transforming Decision-Making with Big Data Analytics: Advanced Approaches to Real-Time Insights, Predictive Modeling, and Scalable Data Integration,” vol. 16, no. 5, 2024.

[4] A. H. Gandomi, F. Chen, and L. Abualigah, “Machine Learning Technologies for Big Data Analytics,” Electronics, vol. 11, no. 3, 2022, doi: 10.3390/electronics11030421.

[5] N. Rane, M. Paramesha, S. Choudhary, and J. Rane, “Machine Learning and Deep Learning for Big Data Analytics: A Review of Methods and Applications,” Jul. 2024, doi: 10.5281/zenodo.12271006.

[6] J. Qiu, Q. Wu, G. Ding, Y. Xu, and S. Feng, “A survey of machine learning for big data processing,” 2016. doi: 10.1186/s13634-016-0355-x.

[7] D. S. Vinayak Pillai, “Techniques for Processing and Analyzing Large Data Sets Using Big Data Analytics,” 2024.

[8] A. K. Aftab Arif, Muhammad Ismaeel Khan, “An overview of cyber threats generated by AI,” Int. J. Multidiscip. Sci. Arts, vol. 3, no. 4, pp. 67–76, 2024.

[9] D. Rao, “Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment,” J. Cybersecurity Inf. Manag., vol. 14, no. 2, pp. 367–382, 2024.

[10] L. Zhou, S. Pan, J. Wang, and A. V. Vasilakos, “Machine learning on big data: Opportunities and challenges,” Neurocomputing, vol. 237, pp. 350–361, 2017, doi: 10.1016/j.neucom.2017.01.026.

[11] M. Shah, P. Shah, and S. Patil, “Secure and Efficient Fraud Detection Using Federated Learning and Distributed Search Databases,” in 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC), IEEE, Feb. 2025, pp. 1–6. doi: 10.1109/ICAIC63015.2025.10849280.

[12] S. Arora and S. R. Thota, “Using Artificial Intelligence with Big Data Analytics for Targeted Marketing Campaigns,” no. June, 2024, doi: 10.48175/IJARSCT-18967.

[13] S. Murri, “Data Security Environments Challenges and Solutions in Big Data,” Int. J. Curr. Eng. Technol., vol. 12, no. 6, pp. 565–574, 2022.

[14] S. R. Thota, S. Arora, and S. Gupta, “Hybrid Machine Learning Models for Predictive Maintenance in Cloud-Based Infrastructure for SaaS Applications,” in 2024 International Conference on Data Science and Network Security (ICDSNS), IEEE, Jul. 2024, pp. 1–6. doi: 10.1109/ICDSNS62112.2024.10691295.

[15] V. Pillai, “Enhancing the transparency of data and ml models using explainable AI (XAI),” World J. Adv. Eng. Technol. Sci., pp. 397–406, 2024.

[16] V. Prajapati, “Role of Identity and Access Management in Zero Trust Architecture for Cloud Security : Challenges and Solutions,” pp. 6–18, 2025, doi: 10.48175/IJARSCT-23902.

[17] K. Rajchandar, M. Ramesh, A. Tyagi, S. Prabhu, D. S. Babu, and A. Roniboss, “Edge Computing in Network-based Systems: Enhancing Latency-Sensitive Applications,” in 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), IEEE, Sep. 2024, pp. 462–467. doi: 10.1109/IC3I61595.2024.10828607.

[18] Y. Kumar, J. Marchena, A. H. Awlla, J. J. Li, and H. B. Abdalla, “The AI-Powered Evolution of Big Data,” Appl. Sci., vol. 14, no. 22, 2024, doi: 10.3390/app142210176.

[19] K. M. R. Seetharaman, “Internet of Things (IoT) Applications in SAP: A Survey of Trends, Challenges, and Opportunities,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 2, 2021, doi: DOI: 10.48175/IJARSCT-6268B.

[20] A. Gogineni, “Multi-Cloud Deployment with Kubernetes: Challenges, Strategies, and Performance Optimization,” Int. Sci. J. Eng. Manag., vol. 1, no. 2, 2022.

[21] S. P. M Shah, “AI/ML Techniques for Real-Time Fraud Detection,” DZone, 2025.

[22] D. D. Rao1 et al., “Strategizing IoT Network Layer Security Through Advanced Intrusion Detection Systems and AI-Driven Threat Analysis,” J. Intell. Syst. Internet Things, vol. 12, no. 2, 2024, doi: https://doi.org/10.54216/JISIoT.120215.

[23] S. Patel, “INTEGRATING MACHINE LEARNING TECHNIQUES FOR BIG DATA ANALYTICS,” Int. J. Adv. Comput. Res., vol. 8, pp. 2760–2763, Jun. 2017, doi: 10.26483/ijarcs.v8i5.4165.

[24] Godavari Modalavalasa, “The Role of DevOps in Streamlining Software Delivery: Key Practices for Seamless CI/CD,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 1, no. 12, pp. 258–267, Jan. 2021, doi: 10.48175/IJARSCT-8978C.

[25] S. Chatterjee, “Integrating Identity and Access Management for Critical Infrastructure : Ensuring Compliance and Security in Utility Systems,” vol. 8, no. 2, pp. 1–8, 2022.

[26] M. Gopalsamy and K. B. Dastageer, “The Role of Ethical Hacking and AI in Proactive Cyber Defense : Current Approaches and Future Perspectives,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 2, 2025, doi: 10.5281/zenodo.14916984.

[27] M. S. Akaash Vishal Hazarika, “EXPLORING FAULT TOLERANCE STRATEGIES IN BIG DATA INFRASTRUCTURES AND THEIR IMPACT ON PROCESSING EFFICIENCY,” Int. J. Comput. Sci. Inf. Technol., vol. 16, no. 6, pp. 35–40, 2024.

[28] H. A. Selmy, H. K. Mohamed, and W. Medhat, “Big data analytics deep learning techniques and applications: A survey,” Inf. Syst., vol. 120, p. 102318, 2024, doi: https://doi.org/10.1016/j.is.2023.102318.

[29] M. S. Akaash Vishal Hazarika, “Blockchain-based Distributed AI Models: Trust in AI model sharing,” Int. J. Sci. Res. Arch., vol. 13, no. 2, pp. 3493–3498, 2024.

[30] S. B. Shah, “Machine Learning for Cyber Threat Detection and Prevention in Critical Infrastructure,” J. Glob. Res. Electron. Commun., vol. 2, no. 2, pp. 1–7, 2025, doi: https://jgrec.info/index.php/jgrec.

[31] T. Li, W. Deng, and J. Wu, “Advanced Machine Learning Applications in Big Data Analytics,” Electronics, vol. 12, no. 13, 2023, doi: 10.3390/electronics12132940.

[32] S. R. Sagili, C. Goswami, V. C. Bharathi, S. Ananthi, K. Rani, and R. Sathya, “Identification of Diabetic Retinopathy by Transfer Learning Based Retinal Images,” in 2024 9th International Conference on Communication and Electronics Systems (ICCES), IEEE, Dec. 2024, pp. 1149–1154. doi: 10.1109/ICCES63552.2024.10859381.

[33] S. S. S. Neeli, “Heart Disease Prediction For A Cloud-Based Smart Healthcare Monitoring System Using Gans And Ant Colony Optimization,” Int. J. Med. Public Heal., vol. 14, no. 4, p. 11, 2024.

[34] S. Mittal and O. P. Sangwan, “Big Data Analytics using Machine Learning Techniques,” in 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2019, pp. 203–207. doi: 10.1109/CONFLUENCE.2019.8776614.

[35] R. Alfred, “The rise of machine learning for big data analytics,” in 2016 2nd International Conference on Science in Information Technology (ICSITech), 2016, p. 1. doi: 10.1109/ICSITech.2016.7852593.

[36] F. Khoshaba, S. Kareem, H. Awla, and C. Mohammed, “Machine learning algorithms in Bigdata Analysis and its applications: A review,” in 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2022, pp. 1–8. doi: 10.1109/HORA55278.2022.9799848.

[37] M. M. Rathore, S. A. Shah, D. Shukla, E. Bentafat, and S. Bakiras, “The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities,” IEEE Access, vol. 9, pp. 32030–32052, 2021, doi: 10.1109/ACCESS.2021.3060863..

Keywords:

Machine learning (ML), big data analytics, data pre-processing, supervised learning, unsupervised learning, reinforcement learning, TensorFlow, Spark, big data application, scalability, real-world applications, emerging trends.