Machine learning (ML) advancements have significantly transformed various domains, notably enhancing diagnostic accuracy and intervention strategies in mental health. Leveraging the potential of advanced ML algorithms, this study introduces a robust approach to predicting mental health outcomes, emphasizing enhanced accuracy and reliability. Our research tackles the complex nature of mental health data by deploying sophisticated models like the HistGradientBoostingClassifier and integrating ensemble methods. Applying feature engineering further refines the model’s capability to discern nuanced patterns in diverse datasets. Our empirical analysis reveals substantial improvements in diagnostic precision, with the HistGradientBoostingClassifier achieving an accuracy up to \(\boldsymbol{99.1\%} \boldsymbol{\pm } \boldsymbol{0.1\%}\) and an ROC AUC of approximately \(\boldsymbol{98.96\%} \boldsymbol{\pm } \boldsymbol{0.05\%}\) . This significantly enhanced simpler models, which showed accuracies and ROC AUC scores around \(\boldsymbol{67.7\%} \boldsymbol{\pm } \boldsymbol{0.1\%}\) and \(\boldsymbol{53.4\%} \boldsymbol{\pm } \boldsymbol{0.5\%}\) , respectively. The comprehensive evaluation across varied demographic and clinical settings underscores the models’ broad applicability and transferability, ensuring their utility in real-world clinical contexts. The results of this study not only advance the field of mental health diagnostics, but they also pave the way for implementing ML-driven approaches in routine clinical practice. Integrating these predictive models can potentially revolutionize mental health diagnostics by providing timely, accurate, and personalized treatment recommendations, thereby significantly improving patient outcomes. This work sets a foundation for future innovations and underscores the pivotal role of ML in transforming mental health care.