Abstract: The rise of artificial intelligence (AI) in healthcare has led to significant advancements in dietary assessment and food intake tracking. This paper presents a comparative study of AI-enhanced tools designed to monitor and assess dietary habits. The study critically examines the capabilities of various AI-powered applications, focusing on their accuracy, usability, and effectiveness in capturing comprehensive dietary data. Tools utilizing machine learning algorithms, image recognition, and natural language processing (NLP) are explored, emphasizing their ability to provide personalized nutrition advice, improve user engagement, and contribute to better health outcomes. Additionally, the paper investigates the integration of these tools with wearable devices and mobile applications to enhance real-time data collection and analysis. The comparative analysis highlights the strengths and limitations of different AI-enhanced dietary assessment tools, offering insights into their potential applications in clinical settings and public health initiatives. The study concludes with recommendations for improving the accuracy and adoption of AI-driven dietary tracking solutions, ultimately aiming to foster healthier eating habits and better nutritional management.