The expansion of Cytosine-Adenine-Guanine (CAG) trinucleotide repeats in the HTT gene is the cause of Huntington’s Disease (HD), a progressive neurological illness. HD results in mental, cognitive, and motor impairments starting midlife. Computational approaches in particular, machine learning (ML) and artificial intelligence (AI) techniques are beneficial in assisting and enhancing the diagnosis and illness monitoring process. This research offers an in-depth analysis of the latest computational approaches for Huntington’s disease, aiming to improve understanding and management by high-dimensional genetic, clinical, and imaging data. A detailed explanation of the various modalities and decision-making mechanisms used is given for each condition. Modern computing techniques and classification for certain clinical characteristics are also included. In most instances, addressing sleep problems linked to several diseases emphasizes their relevance in onset identification. Reliance on particular data sources, small sample numbers, and difficulties scaling to bigger and more diverse populations are the limitations of current techniques. Applications of advanced models are further limited by computing costs and ethical constraints. To improve predictability and equity in future research, it is recommended to expand datasets and incorporate a wider range of data.