This research presents a novel approach to modeling Linguistic Fuzzy Basis Function Networks (L-FBFNs) using Hedge Algebra (HA), which provides a robust framework for handling linguistic variables in fuzzy systems. Hedge Algebra offers a systematic method for quantifying linguistic terms, making it particularly suitable for applications requiring nuanced human-like reasoning. This study integrates HA with FBFNs to enhance their ability to process and interpret linguistic data. We propose a model that utilizes the inherent structure of HA to define fuzzy basis functions, thereby improving the interpretability and performance of the network in complex decision-making tasks. The proposed L-FBFN model is validated through a series of benchmark tests, demonstrating its effectiveness in scenarios where traditional fuzzy systems may struggle. Results indicate that the incorporation of HA allows for more precise control over the fuzziness and granularity of linguistic terms, leading to superior performance in terms of both accuracy and computational efficiency. This work represents a significant advancement in the field of fuzzy logic and its applications, paving the way for more sophisticated and intuitive fuzzy systems in areas such as artificial intelligence, control systems, and data analysis. The findings underscore the potential of HA-based L-FBFNs to bridge the gap between computational models and human linguistic reasoning, fostering the development of more adaptive and intelligent systems.