The transfer markets in football are a dynamic and pivotal period which entails a state of volatility where teams and clubs strategize and aim to strengthen their teams. This is more often than not, preceded by in-depth analysis of data related to hundreds of promising and prospective players to find the perfect replacement for the outgoing player. This analysis currently includes labour intensive tasks of manual video reviews and scouts going to games to watch a prospect play. This paper introduces the Football Player Replacement Finder, a novel approach to reduce the complexity and time required for scouting and acquiring impactful talents by using advanced machine learning models and automated data scraping pipelines. Our system employs supervised models for gauging the performance and price of football players along with clustering techniques for player profiling, enabling stat-by-stat comparison of players. By integrating advanced metrics along with appealing visualisations, our system empowers decision-makers to streamline their scouting process and uncover valuable talents effectively.