Establishing process-structure-property (PSP) relationships is essential for optimizing manufacturing techniques, yet it often requires extensive, costly experimentation. This is particularly true for additive manufacturing (AM), where numerous process parameters complicate the task. Our research introduces an interpretable machine learning strategy to predict and refine the process window for laser powder bed fusion (LPBF), while also delineating PSP relationships. We utilized Gaussian process regression (GPR) to model various inputs, such as process parameters and microstructural features, to predict key mechanical properties. The adaptability of the GPR model, through hyperparameter tuning for each input, facilitates feature selection and enhances model transparency. This methodology not only identifies pivotal factors influencing mechanical performance but also clarifies PSP relationships in additive manufacturing alloys, offering insights for customizing final material properties. Our approach is versatile, applicable across different additive manufacturing techniques and materials, and opens the door to achieving new mechanical properties and deeper PSP understanding.