ABSTRACT Background: Hypertension and Diabetes mellitus are the chronic non communicable diseases (CNCDs) which have emerged as a leading global health problem. They are also known risk factors for different complications, like blindness, vascular brain diseases, renal failure etc. This complication mostly facilitated on peoples with both diabetes and hypertension. Thus, the aim of this study was modeling the longitudinal multivariate effect of fasting blood pressure ,systolic blood pressure and diastolic blood pressure on time to complication of hypertensive diabetic patients , Methods: Hospital based retrospective study was conducted among hypertensive diabetic patients attending a follow-up treatment between 2017 G.C and 2019 G.C at Finoteselam Hospital, Amhara region, Ethiopia. A sample of 206 hypertensive diabetic patients screened who were under follow up at finoteselam hospital. The longitudinal outcomes and the time to event (i.e. complication) data with the separate and joint modeling approach were fitted. Multivariate joint models typically combine multivariate linear mixed effect model for repeated measurements and cox model for time to event out comes. Results: The mean baseline age of hypertensive diabetic patients was 57.23 years. Out of 206 hypertensive diabetic patients 109(53.1%) were females and 97(46.9% males) from 109 females and 97 males 45(21.8%) and 34(17%) were complicated from female and male respectively. Generally 80(38.8%) patients were complicated at any complication.. Multivariate joint model was the best model in the research.in this study economic status ,baseline fasting blood sugar, Residenc place and sex were found to significant effect with log FBS, age place of Residenc and smoking status was found to be a significant effect with log SBP. And age, sex, observation time and place of residence were found to be a significant factor for log diastolic blood pressure. Conclusions: When evaluating the overall performance of both the separate and joint models in terms of model parsimony, goodness of fit, smaller standard error of coefficients , smaller total AIC, and the statistical significance of both the association parameters, the multivariate joint model performs better. Thus, authors concluded that the multivariate joint model was preferred for simultaneous analyses of multiple repeated measurement and survival data in this study. The relationship between the three longitudinal outcomes and the hazard rate of complication was statistically significant Complication is more likely to occur in patients with higher fasting blood sugar, systolic bled pressure and diastolic blood pressure. Key words: complication, hypertensive diabetic patients, multivariate Joint Model, Longitudinal Data, Survival analysis, Cox PH model.