This study explores the relationships between the dependent variables (DV) of anterior cruciate ligament (ACL) length, strain, and force and the independent variables (IV) of activity, height, weight, sex, knee flexion, external rotation, and abduction angles. 10 healthy young adults were recorded performing six motion related activities (each 3 times) - walking, running, crossover cutting, sidestep cutting, vertical jumping with both legs, and single-leg horizontal jumping - utilizing 33 reflective markers and 10 camera motion capture systems. From this data, 9375 observations were recorded for each IV and DV. 42 machine learning models were then fed this information in an effort to establish a relationship between the DV’s and IV’s. CUBIST, GBM, and RF models were found to be the most accurate (R^2: 0.997–0.992 for cross-validation; 0.984–0.775 for test) in predicting ACL length, strain, and force. Women also showed ACL strain and force/body weight (BW) up to 3 times higher than men, particularly during cross over cutting, which may explain why female athletes experience ACL injuries at a rate three to six times more than their male counterparts. External knee moments and muscle forces were excluded from the data entered into the machine learning models. Data collection could have also been altered by skin movement artifacts during motion capture.