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Author(s): Mohammed A. Mamun (corresponding author) [1,2,3]; Firoj Al-Mamun [1,2,3]; Md Emran Hasan [3,4]; Nitai Roy [5]; Moneerah Mohammad ALmerab [6]; Mohammad Muhit [2]; Mst. Sabrina Moonajilin [1] 1. Introduction [...], Background: Diabetes management poses significant challenges worldwide, with individuals often facing increased risks of mental health issues, including suicidal behaviors. While traditional statistical methods have been used to assess risk factors, this study introduces machine learning techniques to predict suicidal behaviors among diabetic patients. Therefore, this study aims to investigate the prevalence and risk factors of suicidal behaviors among individuals with diabetes in Bangladesh using both traditional statistical methods and machine learning models. Methods: A cross-sectional study was carried out with 390 diabetic patients, assessing demographic characteristics, diabetes-related variables, mental health indicators, and suicidal behaviors. Statistical analyses including chi-square tests and logistic regression, and various machine learning models, were employed to examine associations and predict risk factors. Results: This study found that 13.8% of the individuals with diabetes reported lifetime suicidal behavior and 9.0% reported past-year suicidal behavior. Significant risk factors associated with higher rates of suicidal behaviors included younger age, marital status, lower monthly income, higher educational attainment, being insulin users, not having a family history of diabetes, and suffering from anxiety. Among the machine learning models tested, the CatBoost model outperformed others, obtaining low log loss values of 0.45 and 0.32 for lifetime and past-year suicidal behaviors, respectively. CatBoost demonstrated excellent accuracy rates of 83.97% and 88.46%, significantly higher than those of traditional logistic regression models. The most significant predictors identified by machine learning were treatment type, followed by occupation, anxiety, age group, and having chronic diseases. Conclusion: The findings suggest the urgent need for comprehensive interventions and support systems for individuals managing diabetes, particularly those at higher risk of suicidal behaviors. Machine learning offers improved predictive power and the ability to identify complex patterns in risk factors. Collaborative efforts between healthcare providers, mental health professionals, and public health initiatives are essential for prioritizing mental well-being in diabetes management.