People normally experience elevated degrees of mental pressure, expanding their weakness to self-destruction endeavors contrasted with the overall population. The intensification of mental pressure can prompt self-destructive considerations, which are a pivotal forerunner to genuine self-destruction attempts. Customary factual methodologies have shown a restricted association between mental pressure and self-destructive contemplations in people without psychological wellness issues. This study explores the utilization of different machine learning strategies to predict suicidal ideation among individuals working in the field of technology, based on various critical aspects of mental stress in both males and females. The techniques used incorporate Logistic Regression, K-Neighbours Classifier, Decision Tree Classifier, Random Forest, Bagging, Boosting, Stacking, and Multilayer Perceptron. The accuracies range from 77.2% to 82.0%, with Stacking accomplishing the most elevated precision of 82.0%. Besides, these models develop standard measurements, such as the BSRS-5 score by accuracy, sensitivity, specificity, precision, and the AUC of ROC and PR curves. For instance, Boosting showed an accuracy of 81.5%, while Multilayer Perceptron showed a cross-validation AUC of 88.9%. These outcomes feature the capability of machine learning strategies to foresee suicidal considerations among different populations successfully.