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Cotton (Gossypium spp) is one of the most important cash crops in India. The productivity of cotton in the last decade has suffered a great set back due to Cotton leaf curl virus disease (CLCuVD) in Indian Punjab. It has devastated cotton production during the past couples of decades or so causing serious problems in its management. This study, therefore, was initiated to develop a disease predictive model to predict epidemiological factors conducive for disease spread/ incidence. Four years data of CLCuVD incidence, whitefly population density, and environmental variables were collected for the development of a predictive model from the experiments conducted at Punjab Agricultural University, Regional Station, Faridkot, Punjab (India). A close relationship was observed between CLCuVD incidence and whitefly population. A predictive model based the on data (2010-2013) of CLCuVD incidence, whitefly population density, and environmental variables was developed (Y=253.1-11.8* Min T + 3.49 Max T+0.682* Min RH-1.13* Max RH-0.20 RF+1.65* WF popn. ; R2=0.62). The model so developed was validated for the year (2014). Minimum temperature has significant negative and minimum relative humidity along with vector has significant positive, contribution towards the appearance of disease. So if the minimum temperature in the months of June and July is less than 260C to 280C and minimum relative humidity is more than 50%, then chance of appearance of CLCuVD is maximum. There was a moderate difference between the slope of observed and predicted values (5.85 and 6.68) with R2 of 0.82 and 0.78, respectively. It was envisaged that the model would be helpful in forecasting the disease to decide the correct timing of pesticide application, in order to manage CLCuVD effectively.