<p>Neurodegenerative diseases (NDDs) such as Alzheimer’s disease (AD), essential tremor (ET), multiple sclerosis (MS), and Parkinson’s disease (PD) are complex disorders that often exhibit overlapping symptoms, leading to diagnostic challenges. Given the increasing interest in retinal imaging as a non-invasive biomarker for neurodegeneration, this study proposes a fully automated machine learning pipeline for disease characterization using optical coherence tomography (OCT). We analyze macular thickness patterns across three key and relevant retinal elements: retinal nerve fibre layer (RNFL), ganglion cell layer to Bruch’s membrane (GCL-BM), and the total retina. These are processed by two complementary regional layouts: the standard ETDRS scheme and a custom 3<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation>3 quadrant grid. These measurements are used to train multiple classifiers to distinguish between healthy controls and NDDs either collectively or individually. The proposed method processes 34,375 OCT B-scans from 353 subjects and highlights disease-specific thickness patterns with a pathological distinction score ranging up to 0.71 depending on the retinal region, disease, and classifier. Sector-based grids generally outperform quadrant-based ones, revealing highly localized pathological signatures. Our findings demonstrate that each disease manifests distinct retinal alterations, aligning with current clinical literature while offering novel insights for ET and PD. The study reinforces the potential of grid-based OCT analysis as a discriminative and fully automatic screening tool, paving the way for improved early diagnosis and differential analysis of NDDs through retinal biomarkers.</p>