Objective:To evaluate the impact of advanced imaging technologies on the diagnosis, treatment efficacy, and rehabilitation outcomes of sports-related injuries. Methodology:A quantitative research design was employed, utilizing a retrospective analysis of medical records and imaging reports collected over the past 15 years. The study included data from professional, semi-professional, and amateur athletes who had undergone imaging-based injury assessment and rehabilitation. Clinical datasets were gathered from hospitals and sports rehabilitation centers, focusing on the effectiveness of various imaging technologies in diagnosing ligament tears, muscle strains, cartilage damage, and concussions. Statistical methods were applied to analyze the correlation between imaging modalities and recovery outcomes, while AI-based diagnostic tools were compared to conventional radiological assessments. Results:The results indicated that MRI and AI-assisted imaging exhibited the highest diagnostic accuracy, particularly in detecting ligament and cartilage injuries. AI-driven diagnostics significantly enhanced early detection, reduced diagnostic inconsistencies, and improved treatment precision. Professional athletes had greater access to advanced imaging technologies, while semi-professional and amateur athletes primarily relied on ultrasound and CT scans. Recovery timelines varied based on injury type, with ligament injuries requiring prolonged rehabilitation and muscle strains exhibiting the highest recurrence rates. AI-assisted imaging played a crucial role in predicting reinjury risks and optimizing rehabilitation strategies. Conclusion:This study reaffirmed the transformative role of advanced imaging technologies in sports injury management. The findings aligned with previous research, emphasizing the superiority of AI-enhanced diagnostics in improving clinical decision-making and expediting recovery. However, challenges such as accessibility and cost remained barriers to widespread implementation. Future research should focus on making AI-driven imaging more accessible in non-professional sports settings while exploring emerging imaging modalities for enhanced rehabilitation monitoring.