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A small regionally based probabilistic cyclone track estimation method has been developed in this study. The study domain was split into twelve equally spaced areas with the random number of cyclone eye points. The random number of cyclone eye point data was taken from the output of the dynamic climate simulations model MRI-AGCM (Atmospheric General Circulation Model) and d4PDF (Database for Policy Decision-Making for Future Climate Change) cyclone track data, that used to improve the influence measurement analysis and adaptation planning for global warming. The probability theory, especially the Probability Density Function (pdf) under the normal distribution was then applied to the individual small area to find the most probable cyclone track. This virtual cyclone path shows the direction of the present and the future cyclone path that gives more information for the cyclone disaster prevention.