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Human Immunodeficiency Virus prevalence was predicted using cluster analysis technique. A retrospective design was employed. We deployed surrogate measures for social, economic, political and quality of life. Data mining strategies were trailed to gather data. Factors that affect HIV prevalence were represented with surrogate measures by brainstorming activity. Data were taken from world reports from reliable sources. Using an algorithm, data were analyzed using MiniTab software: partitioning, centroid-based, hierarchical and density based methods. This article introduced three contending models on the interplay of the factors towards its influence on HIV prevalence. It culminates with the integration of a holistic model, which can provide a theoretical basis in predicting HIV prevalence. An integrative model was elevated from its substantial form to a more formal application. From HIV prevalence to communicable disease prevalence, this provides a more wide application.