<p>Neurological disorders of the brain and spinal cord affect millions of individuals worldwide and continue to rise in prevalence. Conditions such as Alzheimer’s disease, Parkinson’s disease, epilepsy, spinal cord injury, and neurodevelopmental disorders disrupt cognitive, motor, and autonomic functions, severely impacting quality of life. This review provides an up-to-date and structured examination of neurological disorders and presents novel findings derived from a rigorous search strategy based on Boolean operators and PRISMA-aligned screening. A total of 154 peer-reviewed articles met the inclusion and exclusion criteria and were systematically analyzed. This paper also offers a comprehensive clinical categorization of neurological disorders and outlines their diagnostic and functional challenges. Then, it classifies the architecture of smart assistive technologies across four dimensions—neurological disorders, smart technologies, functional layers, and clinical outcomes—to establish a unified taxonomy for neuro-assistive research. Further, it presents three major smart assistive techniques used for neurological disorders: (i) AI-based techniques, including adaptive neuro-signal decoding algorithms and behavioural anomaly detection using hybrid deep learning; (ii) IoT-based techniques, consisting of context-aware multisensor fusion frameworks and edge-cloud collaborative health networks; and (iii) wearable system techniques that enable continuous, unobtrusive monitoring in real-world contexts. A detailed performance evaluation summarizes key metrics such as Detection Rate (DR%), Precision Rate (PR%), Recall Rate (RR%), and Processing Time (PT), highlighting how parameter variations influence practical deployment. Benchmark datasets are then encapsulated with descriptions of their features, sizes, and access links, enabling dataset-wise comparison and identification of suitable evaluation platforms for future research. This review also identifies current limitations and capabilities of existing smart assistive systems and synthesizes their implications for future directions. By highlighting gaps such as multimodal fusion challenges, data privacy constraints, and the need for adaptive models, this paper proposes a forward-looking framework to make neuro-assistive solutions more clinically accessible. Ultimately, this work advocates for connected, intelligent, and adaptive systems that advance diagnosis, monitoring, and rehabilitation for individuals with neurological disorders.</p>