Every individual possesses a unique signature, serving as a cornerstone for personal authentication and the validation of critical documents or formal procedures. Signatures, classified as biometric identifiers, play a pivotal role in confirming one’s identity, thereby bolstering security measures. Handwritten signatures are crucial in underprivileged areas where digital authentication is scarce. They serve as primary identity verification for banking, government paperwork, and legal agreements. However, traditional offline verification is slow, especially with high paperwork volumes. To overcome the inefficiencies of traditional methods, automated signature verification (ASV) systems have been developed, leveraging advanced technologies to differentiate between genuine and forged signatures. Advanced technologies are utilized by these systems for determining the authenticity of signature images, discriminating between forged and genuine signatures. We present a novel approach in our work that combines the benefits of Fuzzy Logic (FL) and CNN thus enhancing accuracy of signature verification. In our methodology, within the fuzzy logic framework, we employ the Fuzzy c-Means Clustering method. BHSig260 dataset comprising Hindi signatures is used to conduct the experiments. Our hybrid model achieved a significant 89.29% accuracy, validating the potential of our hybridized approach in accurately classifying real and forged Hindi signatures, thus, in return depicting its effectiveness in advancing ASV technology. Moreover, our approach builds upon the accuracy & efficiency of verifying handwritten signatures in case of critical real-world applications.