The integration of automation, connectivity, and advanced analytics in manufacturing enhances productivity but also increases vulnerability to cyber threats including sensor attacks. Sensors—critical to automation—are particularly susceptible to false data injection attacks that can disrupt operations and lead to system failures. Despite advancements in prevention and detection methods, effective post-attack recovery remains an underexplored area—critical to minimizing operational downtime in manufacturing. The research addresses this gap by introducing a novel agent-based recovery modeling approach tailored for manufacturing systems. A reinforcement learning-driven recovery strategy is developed to restore operations efficiently after sensor attacks. The approach is evaluated through two distinct sensor attack scenarios. The recovery agent's performance is benchmarked against a PID controller using key metrics: downtime, throughput, and efficiency. Results demonstrate significant improvements by enhancing the resilience and security of manufacturing systems against sensor attacks.