This chapter provides a comprehensive examination of data analytics and machine learning methodologies, encompassing fundamental processes from data preprocessing and feature engineering to advanced algorithmic implementation. We explore supervised, unsupervised, and reinforcement learning paradigms alongside their practical applications across diverse industrial sectors including healthcare, finance, retail, manufacturing, transportation, education, and energy systems. Contemporary challenges in data quality, scalability, interpretability, and ethical considerations are addressed alongside emerging trends in automated machine learning, deep learning architectures, and federated learning approaches. The chapter synthesises theoretical foundations with practical implementation considerations, offering detailed insights into the transformative potential of data-driven decision-making across multiple domains while addressing evaluation metrics, organisational challenges, and future directions.