Medical imaging, especially X-ray radiography, plays a pivotal role in diagnosing musculoskeletal disorders. However, the clinical diagnosis by radiologists can be labor-intensive and somewhat variable, thus making it attractive to consider deep learning approaches for automating the classification task. In this research study, convolutional neural networks (CNNs) were employed for the classification of musculoskeletal radiographs coming from the MURA data set (5,107 images). Four deep learning architectures—VGG19, Xception, MobileNetV2, and ConvNeXtBase—were benchmarked on a variety of key metrics such as Recall, Precision, and F1-score. MobileNetV2 scored best with its original F1 of 0.92 and achieved an optimized level of 0.94 with the tuning of hyperparameters (learning rate, dropout, L2 regularization). Respective preprocessing, involving automatic-orientation resizing and augmentation data (blurring, rotate, shear, and noise add)) helped to boost generalization. This study confirms the clinical potential of MobileNetV2 in musculoskeletal radiograph classification with good computational efficiency and reliability. The whole undertaking supports radiologists with improved diagnostic accuracy and reduced workload, thus marking a contribution to the advancement of AI in medical imaging concerning the detection of musculoskeletal disorders.