Malnutrition has been a global health issue that can affect anyone at any age, be it kids or elderly. Malnutrition refers to the biological process whereby an organism does not receive enough nutrients or energy needed to thrive, leading to serious diseases. The strategic unification of Artificial Intelligence (AI) in identifying such incidences plays a vital role in early diagnosis and prevention of such issues. This review critically analyzes many recent publications on the applications of AI in the detection of malnutrition in different populations, methodologies and challenges faced. The reviewed studies demonstrate the increasing adoption of Machine Learning (ML) and Deep Learning (DL) methods in malnutrition screening with supervised learning models and classifiers being the most preferred models. Also, image-based approaches using transformations on Convolutional Neural networks (CNN) or various transfer learning approaches based on pre-trained deep learning networks have been focused mostly on detecting child malnutrition. AI-driven frameworks have also been investigated for elderly nutrition monitoring and diet-related diseases diagnosis, but barriers to implementation continue to exist. One of the major conclusions of our review is that; a lot of exciting advances have been reported to develop AI-based models to identify malnutrition, but over 90% of these have been abandoned and are not using clinical practice. The motive for this discrepancy could be multi-fold, including limited dataset availability, differences in dietary patterns, and the absence of validated screening tools across various populations and groups of patients. Moreover, malnutrition detection using AI at hospitals previously was difficult as there was an inconsistency in meal tracking and patient data collection. The review highlights the problem of highly sophisticated systems that are difficult to train but need to be highly accurate and reliable for different populations. Hence, this review is very beneficial for anyone working to fight malnutrition. It connects the dots between cutting-edge AI research and real-world clinical practice, helping us make a bigger impact.