Background <p>Depression is a frequent comorbidity among individuals with chronic diseases, amplifying morbidity and complicating disease management. In Bangladesh, data on the prevalence and predictors of depression in this population remain limited, particularly using advanced machine learning (ML) approaches.</p> Methods <p>This cross-sectional study included 1,222 adult patients with clinically diagnosed chronic diseases, recruited from multiple healthcare centers between May and November 2024. Structured interviews collected information on sociodemographic, lifestyle, behavioral, clinical, and mental health-related factors. Probable depression was assessed with the Bangla version of the Patient Health Questionnaire-9 (PHQ-9). Traditional logistic regression and six ML algorithms were employed to identify factors associated with potential depression and evaluate model performance. SHAP and feature importance analyses were used to interpret ML results.</p> Results <p>The prevalence of probable depression among chronic disease patients was 29.7%. Adjusted regression analysis identified unemployment, urban residence, smokeless tobacco, alcohol and substance use, physical inactivity, short nighttime sleep duration (&lt; 7&#xa0;h), family history of chronic illness, and unmet mental healthcare needs as associated factors. CatBoost outperformed other ML models (accuracy: 71.1%; AUC: 0.76) in depression classification, with feature importance analyses consistently reporting residence, occupation, family history, and mental healthcare fulfillment as the top predictors.</p> Conclusions <p>Depression is highly prevalent among patients with chronic diseases, shaped by a complex interplay of diverse factors. Machine learning models can accurately identify individuals at elevated risk and predictive factors, which can be used for targeted pre-screening and intervention strategies for this vulnerable population.</p> Clinical trial number <p>Not applicable.</p>