The rapid growth of electric vehicles (EVs) has intensified the necessity for advanced battery management system (BMS) capable of ensuring safety, reliability and optimal performance of lithium-ion batteries (LIB). This study presents a comprehensive review and analysis of modern smart BMS architecture with a particular focus on the integration of artificial intelligence (AI) and machine learning (ML) techniques. Key functional aspects including state estimation, cell balancing, thermal management, fault detection, and predictive maintenance are critically examined. The role of AI/ML algorithms such as artificial neural networks, long short-term memory networks, support vector machines, and deep learning modes in enhancing sate of charge (SoC), state of health (SoH), and remaining useful life (RUL) is discussed in detail. Next, the study evaluates emerging trends such as FPGA-based edge implementation and IoT-enabled cloud integration for real-time monitoring and control. A comparative analysis of various AI/ML techniques highlights their pros, cons, and practical deployment challenges. The finding indicates that hybrid approaches combining model-based methods with data-driven algorithms offer the most promising pathway for next-generation BMS. This work provides valuable insight into current technological advancements and identifies key research gaps for the development of scalable, efficient and intelligent battery management solution. Based on a critical assessment of 120 research articles and review papers, this study provides a comprehensive system-level evaluation of AI/ML-enabled BMS for electric vehicles. Unlike previous reviews focusing on individual BMS functions, it integrates and critically compares state estimation, cell balancing, thermal management, fault diagnosis, predictive maintenance, intelligent charging, FPGA-based edge intelligence, and IoT-cloud-enabled battery monitoring while identifying key research gaps and future opportunities.