Timely fault detection in AC motors is critical for maintaining industrial efficiency and preventing costly shutdowns. Condition monitoring techniques, coupled with machine learning applications, exhibit significant potential for enhancing the reliability of fault detection systems. The effective implementation of machine learning algorithms can facilitate prompt identification of faults, thereby minimizing operational losses. A comprehensive review of various machine learning algorithms applied for fault detection in AC motors highlights their effectiveness and the challenges associated with their implementation in real-world scenarios. Additionally, future prospects for developing advanced machine learning-based fault detection systems that can integrate seamlessly with current monitoring strategies are explored, enhancing predictive maintenance and ensuring operational safety. This synthesis aims to provide insights into the transformative role of machine learning in the domain of fault diagnosis, ultimately promoting more efficient maintenance practices for AC motors.