This research employs machine learning techniques and location-based social network data to optimize neighborhood selection for Indian restaurant ventures in Delhi. Analyzing a dataset encompassing 186 neighborhoods and 848 restaurants, clustering techniques highlighted clusters 2 and 3 with the highest Indian restaurant density. Subsequent heat map analysis narrowed the selection to 57 neighborhoods, emphasizing areas lacking Indian restaurants and exhibiting lower cuisine saturation. Proximity to popular locales guided the extraction of 11 prime locations meeting business criteria. Different classifiers were applied, with K-Nearest Neighbors achieving an accuracy of 0.756, Decision Tree at 0.732 and Random Forest leading with 0.805. Notably, Random Forest emerged as the most effective classifier for optimizing restaurant localization strategies. While acknowledging limitations due to dataset constraints and potential omissions of neighborhoods, this study highlights the significance of machine learning in systematically refining restaurant localization strategies within extensive urban landscapes like Delhi.