Sensory evaluation remains the most recognized and widely adopted method for tea quality evaluation, supported by a well-established system consisting of national and industrial standards, reference samples, and certified tea tasters. While this system ensures grading consistency and quality control, its reliance on manual evaluation presents limitations in terms of efficiency and objectivity, particularly under increasing demands for digitalization and large-scale production. Recent advances in multimodal sensory data fusion and artificial intelligence offer promising alternatives for the rapid and intelligent evaluation of the sensory quality of tea. This review systematically summarizes current sensory data acquisition techniques, including electronic nose, electronic tongue, near-infrared spectroscopy, and computer vision. It further outlines different multimodal data fusion strategies, including feature-level, decision-level, and deep fusion methods, and critically analyzes the application of machine learning models such as support vector machines, convolutional neural networks, and ensemble learning in sensory quality evaluation. Key challenges-such as lack of data standardization, limited model generalization, and underdevelopment of portable devices-are also discussed. Finally, this review highlights future research directions for state-of-the-art techniques, including multimodal big data integration, federated learning, and edge computing, to support research and industrial applications of intelligent sensory evaluation of tea.