It’s very important for understanding and responding to customer emotions in real time to have an effective and satisfiable customer service. This study introduces an automated Sentiment Analysis System for Helpdesk Calls, leveraging Long Short-Term Memory (LSTM) networks and advanced Natural Language Processing (NLP) techniques to enhance service efficiency. Traditional sentiment analysis methods mostly fail to capture the nuances of spoken language, which reminds the need for a more robust approach. The proposed system processes helpdesk calls recordings, applying speech normalization, noise reduction, and feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs) before classification. By integrating machine learning and deep learning models, the system provides real-time sentiment insights, allowing operators to prioritize and address calls based on emotional tone. Performance evaluation using accuracy, precision, recall, and F1-score ensures continuous model refinement. This scalable solution and methodology reduce manual effort, enhances customer interactions, and fosters improved satisfaction and loyalty, representing a significant advancement in automated customer service technology.