The environment around online grocery delivery has changed due to the quick growth of mobile commerce and artificial intelligence. However, the majority of current systems lack intelligence support for recipe recognition, ingredient mapping, and interaction with small local stores, instead prioritizing massive retail networks and concentrating mostly on logistics and rudimentary recommendation services. Grocery applications might be greatly improved by recent developments in deep learning, especially convolutional neural networks for food picture identification and natural language processing methods for ingredient extraction. However, these technologies are rarely combined into unified hyperlocal delivery systems and are frequently used separately. This assessment examines current work in hyperlocal delivery optimization, recommendation systems, AI-based food identification, and sustainable logistics frameworks. According to the report, there is a glaring need for integrated mobile architectures that incorporate eco-friendly delivery methods, real-time inventory synchronization, tailored suggestions, and image-based recipe recognition. A hypothetical AI-enabled mobile grocery framework is put forth to fill this gap. It supports two modes of interaction: direct grocery ordering and image-based recipe recognition with automatic ingredient-to- cart mapping. It also gives priority to adjacent small retail establishments. The suggested strategy seeks to improve customer comfort while digitally empowering small merchants within a scalable hyperlocal ecosystem by combining computer vision, natural language processing, hybrid recommendation models, retailer management modules, and sustainable route optimization.