Understanding human social interaction has long been a central challenge across psychology, linguistics, sociology, and computer science. With the increasing availability of sensing technologies, computational models, and machine learning techniques, the interdisciplinary field of social signal processing has emerged as a systematic approach to analyzing, modeling, and interpreting human social behavior through observable nonverbal and verbal cues. This research article presents an extensive theoretical and methodological exploration of multimodal social signal processing, grounded strictly in the foundational and empirical literature provided. Drawing on work in nonverbal behavior analysis, multimodal interaction modeling, organizational behavior sensing, dominance detection, gesture analysis, facial expression processing, voice activity detection, and machine learning classification techniques, the article synthesizes insights across computer vision, signal processing, social psychology, and discourse studies. The study conceptualizes social interaction as a dynamic, context-sensitive process in which meaning is co-constructed through coordinated patterns of speech, gesture, gaze, facial expression, posture, and turn-taking behavior. Particular emphasis is placed on dyadic and group interactions, such as meetings and video-mediated communication, where power relations, politeness norms, emotional context, and cultural expectations shape observable behavior. The methodology section elaborates, in descriptive depth, how multimodal data can be captured, represented, and analyzed using approaches such as bag-of-gestures, pose recognition, face detection, voice activity detection, and supervised learning models including support vector machines and boosting-based classifiers. The results are discussed in terms of interpretive patterns rather than numerical metrics, highlighting how dominance, engagement, interest, politeness, and indirect meaning can be inferred from integrated behavioral signals. The discussion critically examines theoretical implications, cross-cultural considerations, limitations of current approaches, and future research directions, emphasizing the need for context-aware, ethically grounded, and culturally sensitive models. The article concludes by positioning multimodal social signal processing as a crucial framework for advancing human-centered computing, organizational analysis, and the scientific understanding of social interaction.