This study presents a comparative analysis of control strategies designed to enhance the performance of Anti-Lock Braking Systems (ABS) and improve vehicle safety. The research explores three key approaches: First, it evaluates Fuzzy Logic-Controlled ABS, comparing five defuzzification algorithms using MATLAB’s Fuzzy Logic Toolbox. Second, it investigates a Neural Network-Based Fault-Tolerant Control strategy, emphasizing improved fault tolerance during braking. Third, it assesses the performance of three ABS controllers—fuzzy logic, bang-bang, and PID controllers.  The findings reveal that Fuzzy Logic-Controlled ABS significantly enhances braking performance and directional stability, while Neural Networks demonstrate rapid response and accuracy in generating real-time substitute signals, thereby boosting system reliability. Among the controllers, the PID controller excels in reducing stopping distance and time, though the Fuzzy Logic Controller shows superior control over relative slip, enhancing steerability despite longer stopping distances and times. This comparative analysis provides valuable insights into ABS control strategies and their implications for vehicle safety. Future research should focus on refining ABS algorithms, developing robust fault detection mechanisms, and optimizing controller designs to further advance automotive safety and ABS efficiency.