Food Recommendation Based on Health Issues and Climatic Conditions

In an era where health-conscious decision-making is becoming increasingly important, food choices play a crucial role in overall well-being. This paper presents a comprehensive review of food recommendation systems that integrate health conditions and climatic factors. By leveraging machine learning methodologies, particularly Random Forest (RF), the study explores how dietary suggestions can be tailored to individual health needs while considering seasonal variations. Various techniques, including classification algorithms and data-driven approaches, are analyzed to evaluate their efficiency in personalizing nutritional advice. Key studies are examined to compare the effectiveness of different models in providing accurate food recommendations. Additionally, prevailing challenges such as data imbalances and the dynamic nature of dietary preferences are discussed. This study underscores the significance of intelligent recommendation systems in fostering healthier eating habits and outlines future research directions aimed at enhancing their adaptability and precision.