We have increased the maximum force of photomechanical crystals to a level higher than previously reported, aided by two machine learning models. LASSO regression revealed that certain substructures likely have positive and negative correlations with Young's modulus. Substructures that tend to form hydrogen bonds were selected as likely positive correlators, while benzene rings and halogens were identified as likely negative correlators. This enabled us to select appropriate substructures of salicylideneamine derivatives, and the measured Young's modulus of crystals without halogens was consistent with the LASSO trend. Subsequently, we sampled crystals for force measurements using Bayesian optimization. The active sampling achieved a blocking force of 37.0 mN in 110 measurements. Assuming this value represents the global maximum force in the parametric space, the exploration efficiency is at least 73 times higher compared to grid search. This study demonstrates the effectiveness of combining machine learning approaches -LASSO regression for molecular design and Bayesian optimization for efficient sampling in the development of high-performance photomechanical crystals. Our findings not only establish a new benchmark for photomechanical crystal force output but also provide a generalizable framework for materials optimization that could be extended to other molecular crystal systems.