Controlling the blocking force of photomechanical crystals is crucial for their practical applications. While generating small forces can be readily achieved by adjusting light intensity, increasing the maximum force remains challenging as it depends on multiple factors including crystal properties, sizes, and experimental conditions. The relationship between these parameters and the generated force is not yet fully understood. Expanding the achievable maximum force could significantly broaden the potential applications of photomechanical crystals. Therefore, a comprehensive investigation of crystal properties and experimental parameters is essential. However, conducting a systematic grid search is impractical due to the extensive time and resources required for experiments. Machine learning approaches, which have recently proven effective in designing molecular materials and predicting crystal properties , offer a promising alternative. When predicting physical properties, striking the right balance between predictive performance and model interpretability is crucial, with the choice of model depending on the specific objectives. While Bayesian optimization has demonstrated its effectiveness in optimizing experimental conditions across various fields , its application to photomechanical crystals remains unexplored.