The study piece explores the rising suicide rates worldwide trend with a specific focus on the Indian context. By using AI algorithms and data mining approaches, it seeks to predict the root reasons for suicide. The intention is to enable specialists to take preventative action to avert such tragedies by pinpointing the critical elements that contribute to suicide behavior. An innovative LSTM-Attention-CNN model for analyzing internet entertainment content for indications of suicidal thoughts is presented in this study. The suggested model outperforms the current benchmark models with an astounding accuracy of 90.30% and an F1-score of 92.65% after undergoing thorough evaluations. Thanks to machine learning and intense learning, suicidal behavior can now be anticipated and stopped in society (DL). Studies have demonstrated the accuracy with which machine learning algorithms, such as boosting and neural networks, can forecast suicidal ideation and actions. Their efficacy varies, nevertheless, according to the particular suicide outcome. Deep learning architectures like DistilBERT and LSTM were developed in response to worries about the volume of suicide-related content on social media platforms. These architectures are designed to recognize suicidal behavior in online conversations.