This study showcases a technique that employs machine learning algorithmic techniques and emotion recognition to analyze client feedback, thereby improving the overall customer experience. To comprehend the wants of customers as well as their opinions about the company's products is crucial for a corporation to understand the market. There is a growing need to accurately gather feedback from clients. To address the demand for precise feedback analysis, we decided to concentrate on this problem. While offering insightful data on their attitudes and feedback, the suggested strategy protects client privacy. Describing the difficulties in assessing consumer feedback from image data on their faces, the study explains the idea of privacy preservation in machine learning. While clients provide feedback, emotions are extracted from their photos using face detection techniques, and then machine learning algorithms are applied for emotion analysis. To protect privacy, the strategy employs data anonymization techniques such as differential privacy and k-anonymity. By offering justifications and results, the project demonstrates how the suggested strategy enhances customer experience while protecting privacy. The information generated from visuals and emotions is much more accurate and representative of the client's genuine opinions when compared to the more conventional techniques of gathering customer feedback. Companies seeking to enhance customer satisfaction while safeguarding their privacy could benefit greatly from the project's findings.