Title: Machine Learning-Driven Thermal Regulation in Next-Generation Lithium-Ion Batteries for Sustainable Electric Mobility Abstract: This review explores the integration of machine learning (ML) with advanced battery thermal management systems (BTMS) to enhance the performance, safety, and longevity of lithium-ion batteries in electric vehicles (EVs). Within the framework of sustainable energy systems engineering, the paper examines six primary BTMS architectures—air, liquid, refrigerant, phase-change material, thermoelectric, and heat pipe cooling—and evaluates their applicability under fast-charging conditions and tropical climates. Special emphasis is placed on ML-enabled optimization of charging protocols and thermal dissipation designs, highlighting how predictive modeling and Bayesian optimization reduce development cycles and improve system reliability. The discussion extends to region-specific adaptations, such as those implemented in Thailand, addressing challenges in thermal uniformity and energy efficiency. By bridging advanced battery manufacturing with intelligent thermal management, this work underscores the role of adaptive, data-driven systems in achieving scalable and sustainable EV infrastructure.