The persistent challenge of overcrowding in public spaces during events and festivals necessitates innovative solutions for efficient crowd management. In this paper, we propose a real-time crowd detection system employing OpenCV and pyttsx3. Our proposed system offers a practical and efficient solution for overcrowding management, providing real-time insights and alerts to facilitate timely interventions in public spaces. The system utilizes Haar cascade classifiers within OpenCV for accurate and rapid identification of pedestrians and upper bodies in video streams. The project initiates by initializing essential components, including a video capturing device, and enters a processing loop where frames are continuously analyzed. This demonstration by OpenCV and Pyttsx3 is a very fine example of how computer vision together with aural feedback can enhance its advantages in crowd control and monitoring events.