Study and development in the area of natural fiber have increased precipitously over the years due to the advantages such as stability towards environmental influences and cost-effectiveness. These features make them suitable as a substrate material in a wide range of wearable sensing applications. However, selecting suitable natural fibers based on various parameters for a specific sensing application is essential. To develop flexible and wearable sensors, this paper seeks a natural fiber that can serve as a suitable substrate. Multiple physical and chemical properties and parameters are considered to optimize the best selection among a few alternatives using a novel hybridized Multi-Criteria Decision-Making (MCDM) approach called the Entropy Trade off Ranking Method (Entropy-TOR). Using this novel technique, it is found that Bagasse has an alternative weight of 1.659. The performance of the sensor based on Bagasse also reflected the same when compared with other sensors.