This thesis aims to explore the potential of data-driven methodologies in basketball scouting and player performance evaluation, with the objective of enhancing the efficiency, objectivity, and effectiveness of player analysis and decision-making processes in professional basketball teams. The study leverages a specially curated dataset that incorporates performance metrics and personal attributes of athletes, including a regional perspective to identify talent prospects based on their birth region. By utilizing advanced analytics and appropriate tools, professional teams can make more informed decisions, gain valuable insights into player performance and potential, and bridge the gap between traditional scouting practices and modern data-driven approaches. The thesis proposes the development of a comprehensive platform, including the implementation of a dashboard, to provide scouts, coaches, and decision-makers with access to player information and facilitate quick and efficient decision-making processes.