Dysregulated proteostasis in the retina represents a promising avenue for discovering novel therapeutic targets and diagnostic biomarkers for neurodegenerative diseases with ocular manifestations. Advanced mass spectrometry-based proteomics techniques have shown considerable potential in investigating the retinal proteome in diseases such as glaucoma, age-related macular degeneration, diabetic retinopathy, retinitis pigmentosa, as well as Alzheimer’s disease (AD), amyotrophic lateral sclerosis, and Parkinson’s disease. Recent proteomics innovations are overcoming challenges such as limited sample size and protein coverage that previously hindered comprehensive retinal proteome analysis. Notably, the incorporation of artificial intelligence (AI)-driven computational pipelines, including GPU-accelerated deep learning architectures, has markedly enhanced the precision and effectiveness of retinal proteomics. These advances facilitate high-resolution identification of novel protein signatures within large-scale multi-omics datasets. Furthermore, the integration of advanced AI with state-of-the-art big data infrastructures supports the early detection of biomarkers in neurodegenerative diseases with ocular involvement, offering unprecedented disease specificity and sensitivity. In addition to these computational strides, emerging complementary and alternative technologies continue to provide valuable tools for retinal analysis, expanding the potential for biomarker discovery in both ophthalmic and neurodegenerative disorders. The development of novel proteomic workflows is expected to play a critical role in advancing biomarker discovery, not only for ophthalmic conditions but also for other neurodegenerative diseases, including AD. This review summarizes recent advancements in retinal proteomics, with a particular focus on neurodegenerative and ocular diseases.