A predictive model is developed for talent identification in youth swimming with the intention of improving the identification of youth swimmers with the ability to perform in elite competition. This study brings together many physiology and psychological attributes (age, height, weight, VO2 max, motivation, resilience) and performance data (lap times, stroke efficiency) to create an integrated dataset. Analysis of these variables using machine learning algorithms such as regression analysis and decision trees, and determining pattern that associate with future success. Cross validation techniques are used to validate the model accuracy and generalizability on different age groups of trainings levels at which the model can be utilized. This research has some real value for coaches and sports organizations when they try to prioritize and nurture young talent in predatory swimming is to improve outcomes. With data driven insights, this predictive model is able to disrupt how athletes are selected in talent identification using a more systematic and efficient approach to athlete development. Thus, whilst there are many benefits with respect to efficiency, objectivity and resource allocation of a predictive model for talent identification in youth swimming, it is not without its challenges. To use the model to help, not hinder, development, ethical considerations, the corresponding balance between data driven decisions and human judgment, as well as the mental and emotional well-being of young athletes must be carefully managed.