This paper introduces the Multi-Modal Speed Dating (MMSD) dataset, a large-scale corpus designed to support research on romantic impression formation and interpersonal interaction. MMSD contains 1,250 dyadic interactions between 147 Japanese-speaking men and women, along with synchronized multimodal recordings (audio, video, transcripts), detailed profile data, and responses to 33 psychometric scales. Participants engaged in a structured series of speed dates mimicking real-world dating scenarios and rated their impressions—including love and like—at four time points. Final preferences and mutual contact decisions were also recorded. The dataset is suitable for a wide range of research applications, from predictive modeling of romantic impressions based on pre-date attributes to exploratory analysis of verbal and nonverbal behaviors that contribute to romantic outcomes. Prior studies using MMSD have shown that features such as psychological profiles, facial traits, and interaction behaviors are effective for predicting romantic attraction, and that linguistic behavior is particularly predictive of final outcomes. We detail the construction of the dataset, summarize findings from previous analyses, and outline future research opportunities enabled by its richness—including time-series modeling, personalized prediction, and cross-cultural comparisons. MMSD represents a novel benchmark for interdisciplinary research across psychology, affective computing, and human-centered AI, offering deeper insights into the dynamics of romantic connection.