AbstractWe report a prediction model for sunscreen sun protection factor (SPF) and protection grade of ultraviolet (UV) A (PA) based on machine learning. We illustrate with real clinical test results of UV protection ability of sunscreen for SPF and PA. With approximately 2200 individual clinical results for both SPF and PA level detection, individually, we were able to see that active ingredient information can provide accurate SPF and PA prediction rates through machine learning. Furthermore, we included four new factors—presence of pigment, concentration of pigment grade titanium dioxide, type of formulation and type of product—as additional information for the prediction model and were able to see increased prediction rates as results.