This paper investigates the shared and distinct features of language dysfunction across four neurological conditions, including Alzheimer’s disease, stroke, Multiple Sclerosis, and Parkinson’s disease. It evaluates neuroanatomical substrates, clinical manifestations, and emerging speech-based measurement tools. The authors performed a targeted PubMed search (keywords including “language dysfunction,” “speech markers,” “stroke,” “multiple sclerosis,” “Parkinson’s,” and “AI”) focusing primarily on literature published between January 2020 and February 2025 and synthesized findings qualitatively. They report that each population exhibits distinct language changes; AD shows early word-retrieval and semantic decline with both linguistic and paralinguistic alterations that relate to imaging and fluid biomarkers; stroke produces focal aphasias and network disconnection effects detectable with lesion-symptom mapping; MS presents subtle discourse and fluency changes such as altered function-word use, shorter utterances, and slower speech alongside articulation and timing deficits; and PD affects comprehension and high-level production (reduced lexical richness, increased pauses, slower articulation), particularly under higher cognitive load. Across disorders, ML and AI approaches to extract linguistic (lexical, syntactic) and paralinguistic (acoustic, temporal) features show promise for sensitive detection and monitoring, and the authors advance a “last-in, first-out” framework suggesting language networks’ particular vulnerability to aging and neurodegeneration.