Machine Learning for Big Data Analytics: A Comprehensive Review
DOI:
https://doi.org/10.70445/gtst.2.2.2026.199-218Keywords:
Machine learning, Big data analytics, Deep learning, Data Mining, Scalable systems, AI, Real time processingAbstract
This review examines the convergence of machine learning and big data analytics, showcasing how machine learning has contributed to big data analytics and the extraction of valuable insights from large-scale data. It explores the properties of big data, principles of machine learning, scalable algorithms and data pipelines that are backed by distributed and cloud computing platforms. The research explores use cases in healthcare, finance, retail, social media, and smart cities, showing the broad reach of data science. Challenges including scalability, data quality, privacy, and interpretability are discussed, as are emerging areas such as federated learning, explainable AI, and edge computing. It concludes that this integration is critical to future "smart, fast and fair" analytics.
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Copyright (c) 2026 Tsendayush Erdenetsogt, Mehtab Jamal (Author)
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