The technique of video dehazing, which eliminates noise or fog from video systems, has recently drawn a lot of interest because of its potential uses in numerous different industries. Examples include transportation, entertainment, and surveillance. In this research, we offer a video dehazering algorithm that leverages spatio-temporal dark channel pre-dehazering to remove dehazering in movies. The transmission map is calculated by taking use of time anomalies in succeeding frames and spatial coherence in local space. To guarantee continuity of the dehazed video sequences throughout time, our technique also incorporates temporal disparity limitations. On a variety of test datasets, we assessed the suggested strategy and shown that, in terms of objective measurement and visual quality, it performs better than many other methods. The suggested technique may be utilised for real-time video dehazing applications like enhancing outdoor surveillance visibility or enhancing the visibility of hazy movies taken by drones or other types of cameras. In many computer and video applications, dehazering movies represents a significant advancement. In order to increase visibility and the ability to perceive the focal scene, it seeks to eliminate atmospheric haze or fog from video sequences. In this project, we provide a technique for removing haze from movies that estimates the propagation map using a pre-dark channel (DCP). For the purpose of improving transmission prediction accuracy, we additionally include the spatio-temporal coherence of the DCP in the sequential pictures. Additionally, we describe a modified method for reducing picture lighting irregularities, which boosts the effectiveness of the suggested method in challenging lighting situations. Numerous video defogging applications, including surveillance, traffic, and outdoor photography, can employ the method.