Speech recognition technology is used for automated customer service, virtual assistants, and speech-to-text transcription. Due to variables including speaker variances, background noise, and voice signal unpredictability, achieving high accuracy in speech recognition is difficult. Isolated word recognition, which includes identifying certain words uttered alone, has been created as a solution to these problems. This approach simplifies the audio input and eliminates the need for context analysis, making speech recognition simpler to comprehend. The process comprises of the feature extraction step and the classification stage. In order to discriminate between various audio signals, feature extraction entails locating pertinent features from the audio source. These traits might be spectral traits like Mel-frequency cepstral coefficients (MFCCs) or they could be temporal traits like zero-crossing rate and energy. The process of classifying involves dividing up the acquired information into several word classes using machine learning methods. Machine learning (ML) has developed into a powerful technique for word-by word identification, enabling incredibly accurate speech recognition systems. The Hidden Markov Model (HMM) is a frequently employed machine learning approach in solitary word recognition. HMMs are statistical models that can classify data using a probabilistic framework and capture the temporal dynamics of the audio source. Support vector machines (SVMs), neural networks, and trees of choices are further ML methods used in solitary word recognition. Deep learning developments in recent years have improved solitary word recognition systems’ performance even further.