Material handling and distribution is referred to as the movement of material from its very raw form to the final product delivery to the customer or user. The movement of material is expected to be as less as possible as it does not add any value. So, a minimum material handling is very essential to reduce the final cost of the product. The efficient factory layout and route optimization including the scheduling and dispatching of automatic guided vehicles (AGVs), reduced material handling equipment failure rate, etc. play vey essential role in overall minimization of the material handling. This chapter reviews different optimization approaches including Design for Assembly (DFA), goal programming (GP), reinforcement learning (RL), particle swarm optimization (PSO), genetic algorithms (GAs), nonlinear programming (NLP), Artificial Neural Network (ANN), Artificial Immune System, digital twin, multi-objective optimization approaches such as Non-Sorting Genetic Algorithm-II) (NSGA-II), Strength Pareto Evolutionary Algorithm-II (SPEA-II) for material handling in the view point of factory layout, as well as overall supply chain management. In addition, failure mode, effects, and criticality analysis (FMECA), etc. reducing the potential failures of material handling equipment is also discussed along with several cases studies and real-world problems have also been discussed.