Competitive sports require rapid and intense movements, such as jump landings, making athletes susceptible to injuries due to altered neuromuscular control and joint mechanics. Biomechanical features during landings are associated with injury risk, emphasizing proper movement and postural stability. Computer vision techniques offer a time-efficient, noninvasive, and unbiased method to assess jump-landings and identify injury risks. This study proposes a video analysis framework to evaluate jump landing biomechanics in athletes todetermineirregularmovementsandincorrectpostures.It providesadviceandrecommendationstocoachesforinjury predictionandtrainingimprovements.Theproposedframework istestedusingcountermovementjumpvideosof17NCAA DivisionIfemalebasketballathletes.Theresultsindicateda lowMeanAbsoluteError(0.97),highcorrelation(0.89),high averageaccuracy(98.31%)andF1score(0.98),signifyingthe framework’sreliabilityinidentifyinginjuryrisk.