<p>Speech disorders heavily impact communication and quality of life. Speech therapy is an important intervention for people with speech disorders, but its efficacy is generally assessed using subjective clinician judgments like auditory perception and visual examination of articulatory movements. These traditional approaches are not standardized, susceptible to inter-rater variation, and might miss very small neuromuscular gains. This research introduces an objective, machine learning-based approach for measuring speech therapy improvement via surface electromyography (sEMG) signals acquired from the submental, intercostal, and diaphragm muscle groups. Unlike previous research that targets almost exclusively real-time biofeedback, a Random Forest regression model is developed to provide numerical performance ratings based on time- and frequency-domain characteristics. The model has achieved a Mean Squared Error (MSE) of 11.49 and an <i>R</i>² measure of 0.704, reflecting high predictive precision. Categorization of performance into clinically interpretable classes further increases its applicability for therapists. Experimental verification with publicly available sEMG datasets verifies the model’s ability to identify subtle neuromuscular changes, offering a repeatable, non-invasive measure to aid precision rehabilitation. This data-driven method not only closes the gap between subjective clinical opinion and objective outcome measurement but also encourages standardization across therapy environments. Future developments of this research will include model validation on heterogeneous clinical populations and incorporation into wearable devices for real-time monitoring. Through the integration of biomedical signal analysis and AI, this research provides a basis for affordable, personalized, and scalable solutions in speech therapy monitoring.</p>