In the ever-evolving landscape of computer vision, crowd management is paramount in public safety, event management, and urban planning. Traditional image processing techniques are often time consuming and needs manual efforts to execute complex task in realtime. This paper presents a real-time crowd management framework using a custom-trained YOLOv8 model, which dynamically assesses crowd density, redistributes visitors, and optimizes management through adaptive thresholding. This work leverages CrowdHuman open source dataset which includes real-world surveillance footage, achieving an impressive mean Average Precision (mAP) of 94.8% at an IoU threshold of 0.50, and 76.8% across the IoU range of 0.50 to 0.95, significantly outperforms YOLOv5 and YOLOv11. An accurate detection is a vital to achieve betterment in density estimation. This model utilizes Gaussian smoothing to generate real-time heatmaps, enhancing density visualization inorder to gain insights towards a better management and planning. The incorporation of thresholding mechanism enables automatic visitor reallocation to prevent overcrowding. The inference count for crowd divergence is a major aspect in our study to control the flow of people in congestion. There are numerous applications pertaining to this realm viz., heritage site management, transportation, and other urban spaces work, where the immediate intervention of authorities are indeed. The manuscript also conducts a performance evaluation study on various state-of-the-art YOLO variants, and YOLO-V8 outperforms others in this experiment.