Edge devices are experiencing rapid growth and for real-time, with the increasing demand data processing, fog computing, extending cloud computing capabilities toward the network edge, has emerged as a promising paradigm. However, dynamic and heterogeneous nature of fog environments poses challenges in task scheduling, particularly in ensuring fault tolerance and efficient resource utilization. This research presents a comprehensive assessment of machine learning-based fault-tolerant job arrangements in fog computing. The review covers various machine learning techniques employed for fault detection, fault prediction, task allocation, and rescheduling in fog environments. Furthermore, it discusses the challenges, open research directions, and potential future developments in the field.