Tabular data is a fundamental form of information in real-world applications, ranging from finance and healthcare to scientific research. Unlike traditional views that treat tables as isolated structured data, tables are often inherently multimodal—appearing as images, embedded in documents, or coexisting with text and other modalities. My research explores multimodal tabular data learning, aiming to bridge structured tabular knowledge with diverse input forms and tasks. To this end, our work investigates leveraging tabular data as expert knowledge to provide guidance for visual modalities and enable cross-modal transfer learning. We also study more common scenarios where tables appear as images, conducting comprehensive investigations from evaluation to method development for table-based question answering and reasoning. Beyond these works, we extend tabular learning to more general scenarios, developing unified models capable of handling diverse table tasks within a single framework, and further expanding from tables to broader document-level parsing and understanding.