The recent advancements in multimodal large language models (MLLMs) have significantly pushed the boundaries of chart understanding and mathematical reasoning. The integration of transformer architectures has revolutionized the field, enabling more sophisticated processing of visual and textual data. Notably, the development of benchmarks like MultiChartQA and PolyMATH has highlighted the need for models to perform multi-hop reasoning and handle complex visual challenges. These benchmarks not only evaluate current model capabilities but also guide future research by identifying areas where MLLMs still fall short, such as spatial reasoning and high-level abstract thinking. Additionally, the introduction of automated chart generation tools like ChartifyText and scalable data synthesis methods like ScaleQuest have demonstrated the potential for LLMs to transform complex data into intuitive visual representations and generate high-quality reasoning datasets, respectively. These innovations are paving the way for more robust and efficient models that can handle the intricacies of real-world data and tasks. Future directions should focus on enhancing visual comprehension, improving the scalability of data synthesis, and developing more comprehensive benchmarks to further advance the field.