The rapid advancement of large language models (LLMs) has profoundly influenced pathology, demonstrating the potential to enhance diagnostic accuracy and support clinical decision-making. As LLMs have shown great success in processing textual data, their integration with visual models has propelled the development of multimodal large language models (MLLMs). This review synthesizes current research on MLLMs in pathology, focusing on their application to both histopathological images and clinical data. We systematically examine key innovations in integrating diverse data modalities within model architectures. Additionally, we present a comprehensive overview of applications in diagnostic support, text analysis, and image processing while critically addressing challenges such as data annotation, model interpretability, and computational efficiency. Furthermore, we explore future advancements, including cost-effective fine-tuning techniques, dynamic resolution strategies, and the creation of large-scale, high-quality datasets. Through a detailed assessment of the current state of research and emerging opportunities, this review offers valuable insights for researchers and clinicians to fully leverage MLLMs, with the goal of advancing precision pathology and improving patient outcomes.