The remarkable advancement of deep learning has changed the artificial intelligence landscape, and machines are now capable of demonstrating remarkable performance in many areas, such as natural language processing (NLP) and computer vision. Of these many advances, multimodal deep learning has been one of the leading areas of research, which involves combining of heterogeneous modes of data, such as text and images, to develop more intelligent and robust systems. The subsequent chapter examines the theoretical and practical forms of multimodal deep learning with a focus on both text classification and image classification, and how the modalities are integrated to create holistic models. The investigation of traditional machine learning architectures, such as Support Vector Machines (SVMs), is conducted to establish baseline performance. These are used alongside deep learning models that leverage GloVe embeddings, Convolutional Neural Networks (CNNs) combined with LSTM networks, and attention-based architectures optimized using Particle Swarm Optimization (PSO). This way tries to give a complete review of how these models can be applied to real-world challenges such as content moderation, autonomous systems, and sentiment analysis.