The Human Immunodeficiency Virus (HIV) Viral Infectivity Factor (Vif) is a 192-amino acid accessory protein essential to viral replication which counteracts host APOBEC3 proteins. APOBEC3 proteins interfere with the replication of HIV, hepatitis C virus, hepatitis B virus and retrotransposons. Vif is a recent candidate target for therapeutic and preventative interventions in HIV/AIDS yet little is known about its clinical relevance. We describe the results of applying different machine learning algorithms (Apriori, Multifactor Dimensionality Reductor, C4.5, Artificial Neural Networks and ID3) to the search of associations between HIV-1 Vif protein attributes and clinical endpoints. Final iterations showed that the presence of mutations in BC Boxes, APOBEC motifs and Cullin5 binding motifs were together associated with higher initial CD4 T cells while mutations of specific APOBEC motifs coupled with the conservation of other APOBEC motifs were associated with lower historic CD4 T cells. Conservation of specific APOBEC motifs and BC boxes were linked to lower initial viral loads while different combinations of mutations in the Nuclear Localisation Inhibition Signal and BC Boxes were associated with higher historic viral loads. Further scrutiny of these combinations through traditional statistical methods revealed striking differences in both CD4 T cells and viral loads in patients stratified into those having the previous combinations. While artificial intelligence algorithms do not phase out traditional statistical methods, our Artificial Intelligence (AI)-based approach highlights their use at reducing the dimensionality of large and complex datasets and at proposing novel, unimaginable, associations of biological patterns with functional relevance or clinical roles.