Traffic flow modeling is a critical tool for understanding, forecasting, and evaluating transportation systems. This literature review provides a comprehensive analysis of microscopic traffic flow models, which simulate individual vehicle movements to provide detailed insights into complex traffic dynamics. The review traces the evolution of major model categories, including car-following models (e.g., GM, IDM, OVM) and lateral movement models, highlighting their progression from simple stimulus-response mechanisms to more sophisticated behavioral approaches. A central finding is the indispensable role of robust calibration and validation for ensuring model reliability. Despite their maturity and utility in diverse applications, the review identifies persistent challenges. These include the significant demand for extensive, high-quality empirical data, the substantial computational intensity required for large-scale network simulations, and the inherent difficulty in accurately representing the complexity of human driving behavior. The review concludes by highlighting a transformative era for the field, driven by the rise of Connected and Autonomous Vehicles (CAVs) and advancements in Artificial Intelligence (AI) and Machine Learning (ML). These emerging technologies present both opportunities to overcome current limitations and new challenges related to modeling the intricate interactions between human and autonomous vehicles. Future research is expected to focus on developing hybrid modeling approaches that integrate traditional behavioral models with data-driven algorithms, aiming to enhance traffic efficiency and safety through smart infrastructure integration.