In large-scale distributed systems, task partitioning plays a vital role in performance optimization, load balancing, and fault tolerance. Fault-tolerant mechanisms are essential to maintain reliability and efficiency in the face of node failures, network issues, and unpredictable workloads. This paper investigates various strategies for task partitioning in distributed environments, with an emphasis on fault-tolerant methodologies such as replication, checkpointing, and dynamic reallocation. A novel hybrid partitioning model is proposed that adapts based on system feedback and incorporates fault prediction using machine learning. The model is tested in simulated distributed settings, and results show improvement in system availability and reduced recovery time. This extended paper also explores theoretical models, system-level implementations, and case studies to illustrate the real-world applications and limitations of fault-tolerant strategies.