Background: Melanoma is one of the most fatal forms of skin cancer. Defining relevant biomarkers to predict treatment outcome based on immune checkpoint inhibitors (ICIs) is needed in order to increase overall survival of metastatic melanoma patients (MM). Objectives: This study compared different machine learning models in terms of performance to identify biomarkers from clinical diagnosis and follow-up of MM patients, to predict treatment response to ICIs under real-life conditions. Materials & methods: Clinical data from melanoma patients with an AJCC status of III C/D or IV, having received ICIs, were extracted from the RIC-MEL database for this pilot study. Light Gradient Boosting Machine, linear regression, Random Forest (RF), Support Vector Machine and Extreme Gradient Boosting were compared in terms of performance. The SHAP (SHapley Additive exPlanations) method was used to assess the link between the different clinical features investigated and the prediction of response to ICIs. Results: RF showed the highest scores for accuracy (0.63) and sensitivity (0.64) and high scores for precision (0.61) and specificity (0.63). AJCC stage (0.076) showed the highest SHAP mean value, thus being the most suitable feature to predict response to treatment. The number of metastatic sites per year (0.049), number of months since first treatment initiation and the Breslow index (both 0.032) were less predictive, but still showed relatively high predictive power. Conclusion: This machine learning approach confirms that a certain number of biomarkers may enable prediction of treatment success with ICIs.