Artificial Intelligence (AI) and Machine Learning (ML) are rapidly redefining transportation by making mobility systems smarter, safer, and more efficient. This literature review critically assesses recent developments in AI-based technologies for electric and autonomous vehicles (EVs and AVs), focusing on their applications in perception, decision-making, navigation, energy optimization, and real-time analytics. The study categorizes and discusses ML methodologies including supervised, unsupervised, reinforcement learning, and deep learning applied to key use cases such as traffic forecasting, driver behavior analysis, accident detection, eco-routing, and vehicle-to-everything (V2X) communication. It further explores emerging technologies such as federated learning, neuromorphic computing, and explainable AI (XAI) within automotive systems. While the advancements are promising, challenges remain in areas like data privacy, limited edge computing capacity, and the ethical integration of AI. This review highlights key research gaps and outlines future directions for developing scalable, secure, and interpretable AI models for intelligent mobility. By synthesizing cross-domain innovations, the paper provides a robust foundation for interdisciplinary research in AI-driven electric and autonomous vehicle technologies.