HIV infection is for the most part a chronic and asymptomatic disease. To properly monitor the health status of infected individuals it is important to use host and viral surrogate markers as well as pharmacokinetic parameters. Disease progression, assessment of the antiviral potency of the drugs and response to therapy can only be monitored by repetitive measures of viral and host parameters. To prevent the emergence of antiviral drug-resistance, long term side effects and to decide on the appropriate treatment choices, a comprehensive assessment of all contributing factors, medical and non-medical, is necessary. However, the relationship between treatment outcomes with disease markers and other contributing factors is not simple. To date, a model that accurately predicts the likelihood of disease progression or treatment failure in HIV infected patients does not exist. Extending our previous work in this area, we developed temporal Artificial Intelligence models based on Jordan-Elman networks to longitudinally follow viral surrogate markers together with demographics, biochemical and laboratory data to describe the drug-virus-host interactions in over 4000 HIV adult patients. In an international (multi-continent) study of HIV clinical and laboratory data, the profiles of drug-naïve as well as treated patients were evaluated during a 20 year follow-up. Validation of models on a subset of this cohort (n=595) estimated the sensitivity and specificity of treatment success/failure, under different management modalities for individual patients. ROC-curves predicted: virologic success from baseline (ROC=0.871) in drug-naïve previously non-treated patients, switch from virologic success/ failure to failure/success if ever and when (ROC=0.625), switch to virologic success/failure from failure/success within 6 months (ROC=0.722) following a previous switch. This tool may be helpful in the design of longitudinal clinical trials.