Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who is most at risk remains challenging due to the non-linear relationships between hypoglycemia and its associated risk factors. The objective of this study is to evaluate the importance and impact of risk factors related to the incidence of hypoglycemia through an explainable machine learning method. This prospective study enrolled 1306 adults with type 2 diabetes mellitus at a specialized diabetes center. Over three months, participants were asked to do self-monitoring blood glucose measurements and record hypoglycemic events. Nine clinically relevant features were analyzed using five machine learning models. The performance of the models was evaluated by different metrics. The SHapley Additive exPlanation method was used to elucidate how each covariate influenced the risk of hypoglycemia. Overall, 419 participants (32.08%) reported at least one hypoglycemic episode. Our findings highlight the non-linear nature of hypoglycemia risk in individuals with T2DM. Insulin therapy, Diabetes duration (> 13.7 years), and eGFR (< 60.2 mL/min/1.73 m) were the most important predictors of hypoglycemia, followed by age, HbA1C, triglycerides, total cholesterol, gender, and BMI.