Railcar manufacturing is a complex process prone to disruptions, ranging from machine failures to material shortages. Most of these disruptions lead to delays in production and increased operational costs. One of the key challenges is fault tolerance, which has to be maintained with the efficiency and synchronisation of the assembly line. Traditional fault management approaches rely on reactive, pre-programmed responses or manual interventions, not adapting to dynamic changes. This research presents an approach using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, a deep reinforcement learning strategy, for coordinating multiple agents on the assembly line to optimise the enhancement of fault tolerance in railcar manufacturing. Each agent, which is assigned to a different component of the production process in the form of robotic arms, conveyors, or quality control systems, learns and adapts to new fault scenarios continuously so that the system keeps running without any unforeseen issues. Each agent acts based on the global state of the assembly line for efficient resource allocation, dynamic task reassignment, and predictive maintenance. A centralised critic network evaluates the collective performance of the agents, thus enabling coordination and decision-making. Through extensive simulations, this study shows that the proposed coordination framework using MADDPG significantly reduces downtime, improves throughput, and enhances overall production efficiency. Results indicate that MADDPG is a promising solution for fault tolerance in railcar manufacturing, with potential applications in other complex industrial systems.