An important field in digital speech processing is human voice. Speech recognition in humans has long been a hot topic in signal processing and artificial intelligence research. Natural Language Processing (NLP) offers an interdisciplinary subfield called speech recognition that makes it easier for machines to recognize spoken language and transform it into text. To communicate with machines, speech recognition machines has the potential to be helpful. Technology that can have communicates in real time has been made possible. However, there remain additional issues, such as speaker variance due to other factors like age, gender, signal speed, pronunciation variations, noise from the surrounding area etc. Classification of age and gender is important for speech processing. A lot of work has been done to enhance each of these phases in order to get better and more accurate results . The main goal of this analysis is on the integration of machine learning into the speech recognition system. Hence, this analysis presents a novel speech recognition system using Fuzzy neural network. Pre-processing, speech signal segmentation, speech feature extraction, and speaker recognition are the different phases of a speech recognition system. It uses a fuzzy neural network to identify the speaker's age and gender. Accuracy, F1-score, and Precision are used to evaluate the fuzzy model's performance.