Multimodal Machine Learning: Integrating Vision, Language, and Sensor Data for Intelligent Systems

Abstract

Multimodal Machine Learning (MML) combines multiple data sources such as text, images, audio, and sensor inputs to enhance system intelligence. This paper reviews recent advancements in multimodal learning techniques, including transformer-based architectures and cross-modal fusion strategies. It evaluates applications in healthcare diagnostics, autonomous vehicles, and smart surveillance systems. Challenges such as data alignment, model complexity, and computational cost are discussed. The paper highlights emerging trends in foundation models and unified AI systems capable of understanding diverse data modalities.