The study examines injury data from five professional basketball clubs, applying a systematic analysis with steps ranging from data preprocessing, normalization, to statistical modeling to evaluate injury trends. They found that the most common injuries are to the lower limbs, with ankle sprains (30 per cent) and anterior cruciate ligament (ACL) tears (12 per cent) being the most commonly reported injuries. Such injuries stem mainly from high-intensity movement, insufficient neuromuscular control, and repetitive forces acting on the joints and ligaments. It also blames inadequate recovery time between matches, biomechanical inefficiencies and differing injury prevention programs in place at many clubs as pushing injury rates to such highs. It was concluded that in order to prevent injuries to the ACL and knee joint, neuromuscular training, biomechanical assessment and a gradual process for rehabilitation, should be implemented as these represent the key ingredients in training program or methods. Which indicates that targeted strength training, proprioceptive exercises, along with load management strategies, can greatly decrease the incidence as well as the severity of injuries. On the other hand, the synergy between AI-based injury prediction models and comparative studies with European basketball leagues can enable the formulation of data-driven approaches for the assessment of risk profiles and best practices to be adopted in the prevention of injuries. The findings of this study will be useful for sports medicine and basketball injury prevention researchers, and the recommendations would provide guidance for coaches, sports medical teams, or athletic trainers to keep athletes safe and boost performance on the court. Longitudinal injury tracking, AI-assisted biomechanical analysis, and inter-league injury comparisons should be included in future studies to develop evidence-based, well-structured habits in professional basketball players for injury prevention and rehabilitation.