Big Data Analytics with Machine Learning: Challenges, Innovations, and Applications

Abstract

The convergence of big data analytics and machine learning (ML) has revolutionized decision-making across industries, enabling breakthroughs in healthcare diagnostics, financial forecasting, smart infrastructure, and personalized services. This article explores the transformative potential of these technologies while addressing critical challenges such as scalability, data quality, and ethical implications. We examine cutting-edge innovations, including distributed learning frameworks, real-time analytics, and automated machine learning (AML) systems, which enhance computational efficiency and model performance. The discussion highlights applications across healthcare, finance, retail, smart cities, and social media, demonstrating how organizations leverage large-scale data for predictive insights and operational optimization. Additionally, the paper identifies emerging research directions, such as neuro-symbolic artificial intelligence (AI) integration and responsible AI governance, which aim to balance technological advancement with societal accountability. By synthesizing current trends and future opportunities, this article provides a comprehensive roadmap for researchers and practitioners navigating the evolving landscape of data-driven intelligence.

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