Olympic Games are international field and track events hosted within four years periods. Like other events, sprinting is a track event that requires rigorous and focused training. When training is done with little or no understanding of the possibilities of the games, the competition would leave more to be desired. This paper formulates, evaluates and validates a model for predicting the fastest sprinting time of Olympic athletes of 100m race for a-5 season appearances. Dataset was obtained from the Olympic official records of world best performances, typically Gold medalists in sprint for the male category from the inception in 1896 to the 2016 edition. The model was simulated on MATLAB. Cross-validation was done using residuals for whiteness and independence tests and model outputs. The results were evaluated based on Sum of Square Error (SSE), R-Square, adjusted R-Square, and Root Mean Square Error (RMSE) and benchmarked with existing models. The model outperformed the existing models with higher accuracy and goodness of fit. This prediction is a reasonable guide for predictive training, forecasting and future study on predictive algorithms.