Modern machine learning (ML) and deep learning (DL) techniques are combined with sensory techniques like electronic nose and tongue, spectrum imaging, and color recognition to conduct an in-depth analysis of different types of brewed tea. This research proposes a novel technique for comprehensively evaluating tea, including its classification, nutritional profiling, and health benefits, that challenges conventional assessment methods. The complexity arises from the great range of teas available and their potential effects on health. In this study, we combine state-of-the-art ML and DL methods with sensory instruments for an in-depth analysis. The paper begins with a thorough literature review that reveals the technological development of tea analysis, the benefits and drawbacks of current approaches, and opportunities for future research and development. Powerful ML and DL algorithms are deployed and supplemented with sensory data, all on top of a comprehensive collection of high-definition tea photos. The accuracy shown in classifying and identifying teas is a testament to the efficacy of the proposed method. Further, these algorithms accurately identify nutritional components and evaluate health benefits, bringing a new angle to the study of tea. Accurate data collection, model selection, and sensory data integration are highlighted, and a delicate balance between interpretability, robustness, and computing efficiency is emphasized. The research concludes by highlighting the promise of this multi-modal approach in practical applications within the tea business, despite the difficulties inherent in real-time implementations. This ground-breaking work integrates technology and the food sciences, suggesting potential uses for other consumables.