Poor nutrition is a critical modern health problem, strongly linked to non-communicable diseases (NCDs), such as obesity, type 2 diabetes, and cancer [1, 2]. As general and population-wide diets often fail due to individual characteristics and responses, personalised and precision nutrition (based on genetic and lifestyle factors) are essential, particularly for those with chronic conditions. Advancing personalised nutrition increasingly relies on Artificial Intelligence (AI) and Machine Learning (ML). These technologies power recommendation systems by recognising food and ingredients, and can also be used for disease detection [3]. To make these systems work, large individual health datasets must be collected, including metrics such as heart rate and body fat. This Practice Abstract addresses Recipe Recommendation Systems, one of several recommenders in personalised nutrition. These systems can help individuals maintain healthier diets by suggesting tailored recipes and can also use personal data and image recognition to deliver accurate suggestions.