<p>The advancement of vehicle safety through advanced driver assistance systems (ADAS) depends on the efficient combination of machine learning (ML) and computer vision (CV), but the quick development of algorithms makes it difficult to evaluate and use them in settings with limited resources. A thorough analysis of more than 80 important and current experiments is presented in this work, along with a structured taxonomy that groups ML and CV techniques based on how they are used in important ADAS tasks such driver monitoring, environmental perception, and vehicle control. Our synthesis highlights the feasibility of hybrid models that strike a balance between performance and efficiency. While deep learning models achieve state-of-the-art accuracy in perception tasks, their high computational demand frequently renders them impractical for real-time embedded systems. This analysis offers a strategic roadmap for future research targeted at creating more durable, dependable, and commercially scalable ADAS technology by highlighting important research needs in multi-sensor fusion, domain adaptation for unfavorable situations, and safety validation.</p>