The main ideation of this paper is to know the location where the image had taken from the image with exif and without exif meta data and plot on the map. As we know with the increasing volume of digital imagery shared through online platforms, there is a growing research interest in identifying the geographical origin of an image even when no embedded geotags or Exif metadata are available. This work introduces a novel approach for estimating image locations from image EXIF Meta data if exits or from visual content, eliminating the totally dependent on GPS data. The proposed framework combines object detection and semantic understanding to infer spatial information from contextual features within an image. Using the YOLOv8 model, key elements such as landmarks, objects and scene are first detected. These visual cues are then interpreted through the CLIP (Contrastive Language–Image Pretraining) model, which maps both image and text features into a shared embedding space. By applying cosine similarity between image and textual location embeddings, the system identifies the most plausible location description that corresponds to the given image.