Anterior cruciate ligament (ACL) tears are a frequent cause of knee instability, yet magnetic resonance imaging (MRI) interpretation remains time-consuming and observer-dependent. This paper presents an automated MRI framework for ACL injury screening and severity grading using a hybrid support vector machine–artificial neural network (SVM–ANN) model. A balanced dataset of 600 sagittal knee MRI images from Hospital Taiping (normal, partial tear, complete tear) was standardized via resizing, region-of-interest cropping, contrast enhancement, noise filtering, and segmentation. Morphological and texture features were extracted and reduced using principal component analysis (PCA). The SVM performs the initial screening (injured vs. non-injured) and samples predicted as injured are passed to the artificial neural network (ANN) to classify severity. Using confusion-matrix and receiver operating characteristic (ROC) evaluation, the proposed system achieved 86.2% overall accuracy and 81.7% sensitivity, with the ANN reaching approximately 95% accuracy on injured cases forwarded for grading. A clinician usability survey indicated high acceptance (~95%), supporting the feasibility of deployment as a lightweight decision-support tool. Limitations include reliance on single sagittal slices and single-sequence data; future work will incorporate multi-slice/3D and multi-sequence MRI to improve sensitivity and generalizability.