Cervical Spondylosis (CS) is a progressive, age-related degenerative disorder of the cervical spine that is increasingly aggravated by modern lifestyles characterized by prolonged poor sitting postures.Early and accurate detection is crucial to prevent severe pain, neurological complications, and reduced quality of life. However, variability in imaging interpretation and overlapping pathologies pose persistent diagnostic challenges, even with advanced imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT). This study aimed to evaluate and compare the diagnostic performances of traditional machine learning (ML) models and deep learning (DL) architectures, focusing on a hybrid Inception-ResNet framework. A dataset comprising 376 documents and 3453 contributing authors was analyzed to assess algorithmic performance. Experimental results demonstrated that ResNet-34 (Oblique View) achieved the highest accuracy of 95%, outperforming lateral of 90% and anterior–posterior of 87%) views, whereas traditional ML classifiers such as SVM of 80.4% and KNN of 71.1% lagged. Bibliometric analysis confirmed that China and the USA were the leading contributors, with strong international collaborations of 24.7%.The findings conclude that hybrid Inception-ResNet models significantly enhance feature extraction, speed, and robustness, making them promise for real-world clinical applications and generalizable to diagnostic workflows.