To continue operations of the inland waterway transportation system (IWTS), the interconnected infrastructure, such as locks and dam systems, must remain in good operating condition. However, as the IWTS ages, unexpected disruptions increase, causing significant transportation delays and economic losses. To evaluate the impacts of IWTS disruptions, a Python-enhanced NetLogo simulation tool is developed, where extreme natural events are also considered and characterized by a spatiotemporal model. Utilizing this tool, optimal maintenance strategies that maximize cargo throughput on the IWTS are determined via deep reinforcement learning. A case study of the lower Mississippi River system and the McClellan-Kerr Arkansas River Navigation System is conducted to illustrate the capability of the developed simulation and machine learning-based method for IWTS maintenance optimization.