As the wine industry evolves and consumer demand for quality perception intensifies, the sensory evaluation methods heavily reliant on human experience are increasingly limited by subjectivity and lack of standardization. In this context, evaluation frameworks integrating multimodal sensory data fusion and artificial intelligence (AI) modeling have emerged as a promising frontier in wine flavor assessment. This review provides a comprehensive overview of major sensory data acquisition techniques, including electronic nose, electronic tongue, near-infrared spectroscopy, image analysis, and chromatography-mass spectrometry. It further examines the application characteristics of early, middle, and late fusion strategies in flavor modeling, and evaluates the strengths of AI algorithms in wine flavor recognition and quality prediction. Despite these advancements, key challenges remain, such as difficult integration of heterogeneous data, limited model generalizability, and the absence of standardized sensory lexicons. Finally, the review outlines future directions including preference-driven intelligent evaluation, standardized flavor map development, and real-time detection platforms.