There is a need to improve the prediction of tropical cyclone (TC) rainfall as climate change has led to increased TC rainfall rates. Enhanced reliability in predicting TC tracks has paved the way for statistical methodologies to utilize them in estimating current TC rainfall, achieved by identifying similar past TC tracks and obtaining their corresponding rainfall data. The widely used Fuzzy C Means (FCM) clustering algorithm, though popular, has limitations stemming from its clustering-centric design, hindering its ability to pinpoint the most appropriate similar TCs. Our study introduces the Sinkhorn Distance as a novel measure of TC similarity in rainfall prediction. Our findings indicate that the incorporation of Sinkhorn Distance significantly enhances the accuracy of TC rainfall predictions across WNP. When compared to the conventional approach using FCM, our Sinkhorn Distance-based methodology consistently yields better results, as demonstrated by metrics like RMSE and correlation coefficients. Collectively, the inclusion of Sinkhorn Distance stands as a valuable addition to our toolkit for discerning similar TC tracks, thus elevating the precision of TC rainfall predictions. With ongoing advancements in statistical and AI techniques, we anticipate even more refined approaches to further enhance our predictive capabilities. This study represents a leap forward in meeting the critical need for more accurate TC rainfall forecasts in the WNP Region.