Despite the frenetic pace of e-commerce growth, over 80% of grocery sales remain carried out in physical retail environments around the globe. This traditional retail structure causes a number of serious challenges: frenzied product searches, crowded aisles, and long lines at the tills-all of which severely affect customer satisfaction. On account of such deficiency mentioned above, here we are presenting TechCart: an intelligent shopping framework to help improve the in-store shopping experience of a customer who utilizes AI, deep learning, and wireless communication technologies. Integration of independent navigation, immediate item recognition, personalized recommendations, and budget management takes place via a high-level mobile application. The architectures of this system are: the intelligent shopping cart, the mobile application, and the central server that make easy intercommunication and information exchange between these parts happen. From the results of evaluation, TechCart could identify 5,351 objects with a Mean Average Precision of 84.59%. This, therefore, means that this application is also full of producing results with strong accuracy in object detection. The grocery items commonly detected have a realized accuracy of 95%. The average time saved in shopping was 30% as perceived by the users.