Multiple sclerosis (MS) represents a chronic autoimmune disease involving the central nervous system through inflammation and progressive neurodegeneration. Monitoring disease progression, specifically in terms of neuroaxonal loss, has critical importance in guiding treatment and improving patient outcomes. This review discusses the evolving role of optical coherence tomography (OCT) and artificial intelligence (AI) in monitoring MS progression, highlighting their efficiency in assessing neuroaxonal damage, predicting disability, and complementing conventional imaging techniques. We synthesize current evidence on OCT and OCT angiography (OCTA) metrics, including peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell/inner plexiform layer (GCIPL) thickness, and their correlations with disability, cognitive impairment, magnetic resonance imaging (MRI) findings, and clinical progression. We further review machine learning and deep learning applications for OCT-based classification, segmentation, and prognosis in MS. Thinning of pRNFL and GCIPL correlates with higher expanded disability status scale, brain atrophy, and impaired cognition, often preceding clinical signs of progression. OCTA reveals reduced retinal capillary density in MS, supporting a vascular component in disease pathology. AI tools enhance OCT analysis through automated layer segmentation and predictive modeling, enabling individualized risk stratification and early detection of secondary progression. Multimodal AI frameworks that combine OCT with MRI and clinical data further enhance prognostic accuracy. In summary, OCT and AI are transforming MS disease progression tracking by providing affordable, non-invasive biomarkers reflecting neurodegeneration and cerebrovascular dysfunction. For achieving their full potential as predictive and therapeutic tools in individualized care for MS patients, standardization, external validation, and integration into clinical practice are essential.