Lumbar spondylosis is a prevalent chronic illness that results in deformation of the lumbar spine and limits human movement. Over time, spinal deformities can compress or exert tension on the nerve roots, resulting in lower back discomfort and disc herniation. The incidence of spondylosis is escalating, attributed to a growing population of younger individuals. This tendency results from alterations like contemporary jobs and education. X-ray imaging of the lumbar spine is widely utilized and endorsed by several physicians for its rapidity, precision, and accessibility across diverse patient populations. This article introduces a technique for detecting and classifying both abnormal and healthy lumbar spine X-ray pictures. After image filtration, we implement Knowledge Distillation, wherein a trained teacher model instructs smaller student models. We employ EfficientNet-B4 as the Teacher model, a high-accuracy and efficient Convolutional Neural Network (CNN) architecture for medical image analysis, and MobileNetV2 as the Student model, which also utilizes the knowledge distillation approach. To assess the model’s performance, 2,000 lumbar spine X-ray pictures were obtained from Kien Giang General Hospital and Trung Cang General Clinic, with 872 samples designated for training and testing. The outcomes attained an accuracy of 91.0%, a precision of 90.0%, a recall of 91.8%, and an F1-score of 90.9%. The findings were achieved after 500 training epochs with a learning rate 0.001. This indicates that our suggested model has strong performance with excellent dependability.