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Stress and emotion assessment play a pivotal role in healthcare, aiding in the early detection and prevention of various diseases. In this research paper, we propose a novel healthcare model that harnesses the power of artificial neural networks (ANN) and discrete wavelet transform (DWT) to assess stress and emotion through speech signal recognition. The proposed model extracts essential features from speech signals using DWT and employs ANN for the classification of stress and emotional states. Both ANN and DWT are established signal processing techniques that find applications in diverse domains. We present a comprehensive analysis of our healthcare model, which effectively analyzes speech signals to identify the stress and emotional states of individuals. The model's performance is rigorously evaluated using a publicly available dataset comprising speech signals recorded from subjects experiencing varying stress and emotional states. Experimental results demonstrate that the proposed model achieves high accuracy and surpasses existing state-of-the-art techniques. By employing recurrent neural networks (RNN), we visualize speech's phonetic characteristics, such as Mel-frequency cepstral coefficients (MFCCs), transforming them into sequences of phonemes or words. RNNs, designed to handle sequential data, form the basis of our speech recognition using neural networks, contributing to the model's effectiveness and accuracy. [ABSTRACT FROM AUTHOR]