Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H2 fromDesulfovibrio fructosovorans [NiFe] hydrogenase as a training set. [NiFe] hydrogenases are receiving increasing attention in biotechnology due to their high efficiency in the generation of H2, which is considered by many to be the fuel of the future. However, some of these enzymes are sensitive to O2 and CO. Many efforts have been made to rectify this problem and generate air-stable enzymes by introducing mutations that selectively regulate the access of specific gas molecules to the catalytic site. Herein, we showcase the performance of PathInHydro for the identification of unbinding paths in different test sets, including another gas molecule and a different [NiFe] hydrogenase, which demonstrates its feasibility for the trajectory analysis of a diversity of gas molecules along enzymes with mutations and sequence differences. PathInHydro allows the user to skip time-consuming manual analysis and visual inspection, facilitating data analysis for MD simulations of ligand unbinding from [NiFe] hydrogenases. The codes and data sets are available online: https://github.com/FarzinSohraby/PathInHydro.