This thesis investigates the predictive modeling of suicidal behavioramong older adults in Sweden through the application of machine learning and survival analysis to data from Swedish National Registries. Theresearch utilizes longitudinal and static data characteristics, includingprescribed psychopharmaceuticals, medical conditions, and sociodemographic factors. By incorporating various survival analysis models suchas the Cox Proportional Hazards Model (CoxPH), Random SurvivalForest (RSF), and Gradient Boosting Survival Analysis (GBSA) withsequential machine learning techniques such as Long Short-Term Memory (LSTM) networks, the study aims to enhance the predictive accuracy of suicidal tendencies among the elderly. The thesis concludes thatcombining these techniques provides insights into the risk of suicide attempts and completed suicides while also underscoring the challengesassociated with modeling national registry data and demonstrating anovel approach to addressing suicidal behavior.