Speech disorders present a considerable challenge within the realms of clinical diagnosis and therapeutic intervention, thereby necessitating the development of practical tools for the analysis and monitoring of speech patterns. This mini project, entitled "AI-Powered Speech Analysis Tool for Speech Therapists," was developed to integrate advanced technology into the diagnostic process and provide an innovative solution for therapists. The system enabled therapists to record patients' speech, analyze essential acoustic features such as pitch, speech rate, volume, and articulation patterns, and visualize the results through intuitive graphical representations, including spectrograms and pitch contours. It further facilitated comparative analyses against normative speech patterns, identified anomalies, and suggested potential speech disorders based on the derived analyses. Developed using the Python programming language and leveraging libraries such as librosa for audio processing, matplotlib for data visualization, and SQLite for data storage, the tool ensured broad accessibility and expandability. Diagnostic suggestions, derived from either rule-based systems or machine learning models, augmented its utility, while strict adherence to data privacy regulations safeguarded patient confidentiality. The application holds the potential to be extended into a web-based interface employing frameworks like Flask or Django, thereby enhancing its accessibility for therapists. This project demonstrates technical proficiency in speech processing and data visualization and contributes to the field of speech-language pathology by improving the accuracy and efficiency of speech disorder diagnosis. By facilitating the monitoring of patient progress and enhancing therapeutic outcomes, this tool presents opportunities for further advancements in AI-powered healthcare applications.