This study introduces a novel framework for the automatic two-dimensional tracking of padel games using monocular recordings. By integrating advanced Computer Vision and Deep Learning techniques, our algorithm detects and tracks players, the court, and the ball. Through homography, we accurately project detected player positions onto a two-dimensional court, enabling comprehensive tracking throughout the game. We tested the proposed algorithm using amateur video recordings of padel games found in literature. This approach remains user-friendly, cost-effective, and adaptable to various camera angles and lighting conditions. This makes it accessible to both amateur and professional players and coaches, providing a valuable tool for performance analysis. Additionally, the proposed framework holds potential for adaptation to other sports with minimal modifications, further broadening its applicability.