Multimodal learning is an emerging field in artificial intelligence that aims to enhance machine understanding by integrating multiple data types, such as images, text, audio, and video, to create comprehensive representations of objects or concepts. This approach leverages the unique strengths of each data modality to improve predictions and classifications, exemplified by models like CLIP, which uses contrastive learning to process image and text pairs. CLIP and similar models have advanced the development of zero-shot models for computer vision tasks and are evaluated based on their performance in tasks like image classification and visual question answering. These models are trained to minimize the distance between similar data pairs while maximizing the distance between dissimilar ones, using techniques like contrastive learning. The field of multimodal learning is rapidly evolving, offering significant potential to revolutionize how computers perceive and interact with the world, with diverse applications across industries and a growing interest among researchers and practitioners.