The recent advancements in multimodal machine learning have significantly pushed the boundaries of integrating visual and textual data, leading to innovative approaches in various domains. A notable trend is the shift towards decoder-only models, which streamline the integration of visual and textual inputs, enhancing efficiency and scalability. These models, such as those employing adaptive input fusion and novel attention mechanisms, are setting new benchmarks in tasks like visual question answering and image captioning. Another emerging area is the enhancement of large language models (LLMs) with multimodal capabilities, enabling them to process and generate both text and visual data. This is achieved through innovative instruction tuning techniques and the incorporation of structure-based encoders, significantly boosting performance in protein understanding and multimodal machine translation. Additionally, the development of open-source frameworks and models tailored for specific languages and tasks, such as Thai language processing and protein function prediction, is democratizing access to advanced AI technologies. These developments collectively indicate a robust and inclusive growth trajectory in multimodal learning, with a focus on efficiency, accessibility, and performance enhancement.