The increasing complexity of food consumption patterns presents both challenges and opportunities for public health nutrition. This research leverages big data analytics to gain actionable insights into dietary behaviours, aiming to enhance public health nutrition strategies. With the proliferation of digital health records, social media data, and food tracking applications, vast amounts of information on dietary habits are now available. This study utilizes advanced big data techniques, including machine learning algorithms and data mining methods, to analyze food consumption patterns at a granular level. By integrating diverse data sources such as electronic health records, food diaries, and socio-demographic data, this research uncovers trends and correlations that traditional methods may overlook. The analysis focuses on identifying dietary patterns, nutritional deficiencies, and the impact of socio-economic factors on food choices. Advanced analytics, such as clustering and predictive modeling, are employed to segment populations based on their dietary behaviours and predict future trends in food consumption. The findings aim to inform targeted public health interventions and policy decisions by providing a clearer understanding of dietary patterns and their health implications. This research also explores the effectiveness of personalized nutrition recommendations based on big data insights. Ultimately, the study seeks to bridge the gap between data-driven insights and practical applications in public health nutrition, contributing to improved dietary guidelines and interventions that address the needs of diverse populations. Through this approach, public health professionals can better address nutritional challenges and promote healthier eating habits across different communities.