In the rapidly evolving landscape of sentiment analysis, the integration of facial recognition technologies has proven to be an invaluable asset for understanding emotional responses. This project, "Sentiment Analysis by Facial Detection Using Haar Cascade and CNN," focuses on harnessing advanced machine learning techniques to accurately interpret human emotions through facial expressions. Central to this system is the Haar Cascade classifier for efficient and reliable face detection, which identifies faces within images and video streams. Coupled with a Convolutional Neural Network (CNN), the system effectively classifies emotions such as happiness, sadness, anger, and surprise, providing deep insights into user sentiments. The implementation of this framework enables real-time analysis of emotional states, facilitating applications in areas such as marketing, customer feedback, and mental health monitoring. The system outputs both visual representations of detected emotions and corresponding sentiment metrics, offering a comprehensive view of emotional dynamics without reliance on extensive datasets. This project aims to equip researchers and practitioners with a powerful tool to enhance understanding of human emotions through cutting-edge AI and machine learning technologies.