In recent years, some restaurants have started to utilize tablet computers for food ordering. These trends have great potential in improving restaurant service and increase customer satisfaction. However, there are a variety of food items being offered by the restaurant and this had caused that choosing food items from the menu to become more difficult for restaurant goers especially for those who visit the restaurant for the first time. Sometimes, the restaurant servers are not always helpful in recommending what food items to order when we are at the restaurant. Therefore, a restaurant menu recommendation application, YumPath is proposed to assist restaurant goers in selecting food items from the menu that satisfy their preferences such as healthy options, nutrition and allergies. YumPath is a mobile application that can be used by both restaurant owners and restaurant goers in the restaurant. Association rule mining will be implemented in this application for the recommendation engine. The recommendation engine will collect the restaurant goers' order information and study the data collected to generate the list of rules. Then, restaurant goers will get their food recommendations based on their preferences when they wish to make an order in the restaurant. At the same time, the restaurant owner can view the analytics for the popularity of food based on the order made by the restaurant goers.