The quest for healthy living has made food identification and nutrition prediction crucial, thanks to developments in artificial intelligence and machine learning. This survey article provides a thorough analysis of the state-of-the-art approaches and technologies in the field of food detection, including image recognition methods and how they are used to identify food items from visual data. Furthermore, it investigates nutritional prediction models that use machine learning algorithms and databases to estimate the calorie and macronutrient composition of foods that are observed. It also looks at how these technologies might be used to provide individualized workout and food suggestions. This includes using wearable technology and smartphone apps that provide personalized recommendations and real-time feedback depending on a user’s fitness level and eating habits. Accuracy, user compliance, and data privacy are just a few of the problems and constraints that the article draws attention to, along with future directions and possible advancements in these related domains. This study attempts to provide a thorough overview of how food detection and nutrition prediction technology might be used to support individualized health and wellness goals by summarizing recent research and advances.