Disease forecasting and early warning are essential for better management of plant diseases. Being an important component of the disease triangle, weather parameters and soil characteristics are to be critically considered for developing prediction models for soil-borne diseases. An experiment was conducted during summer, kharif and rabi of 2020 to study the seasonal variation of the natural occurrence of sesame root rot disease, their correlation with weather factors and the derivation of a prediction model. Variations in root rot pathogen inoculum concerning soil depth, season, nutrient content and interaction with other rhizosphere microbes were studied. The experiment revealed that the disease initiated after 3 weeks of sowing and appeared throughout the seasons ranging from 5.00% to 34.6%, 0- 19.8% and 0-26% in summer, kharif and rabi season respectively with an increasing trend towards maturity. High disease incidence was recorded in summer when temperature ranged from 32.1℃ to 42℃ coupled with less to no rainfall followed by rabi and Kharif. Temperature showed a positive correlation with the disease incidence in all the seasons whereas rainfall is negatively correlated. The pathogen inoculum was observed more at 5-10 cm soil depth in the summer season. Prediction models were developed to forecast the disease by employing weather data and current disease incidence using statistical tools. Disease forecast using proposed models can be considered as a prototype for early warning of root rot disease.