Meningiomas, the most prevalent primary central nervous system tumors, present a significant challenge in neuro-oncology due to their variable clinical behaviors and recurrence rates (1). While magnetic resonance imaging (MRI) remains the primary diagnostic tool, recent advancements in our understanding of meningioma genetics have highlighted the critical role of molecular profiles in determining tumor behavior and treatment outcomes (2). This thesis presents a comprehensive exploration of the intersection between imaging features, genetic biomarkers, and artificial intelligence in meningioma management, with the overarching goal of enhancing diagnostic accuracy, treatment planning, and prognostication.The work is structured in four interconnected chapters, each addressing a crucial aspect of this multifaceted challenge: Chapter 1 introduces a novel, large-scale dataset comprising 3,101 pre-processed, multi- sequence MR images along with corresponding genetic and demographic data from patients with histopathological confirmed intracranial meningiomas. This dataset serves as the foundation for subsequent analyses and model development, offering researchers an unprecedented resource to investigate imaging-genetic correlations in meningiomas. Building upon this dataset, Chapter 2 presents the development of a machine learning model designed to predict genetic mutation status in meningiomas using preoperative multi-sequence MRI. By combining radiomics features, convolutional neural network (CNN) outputs, and clinically informed features, this approach demonstrates the potential for non-invasive assessment of genetic biomarkers, which could significantly impact clinical decision-making, especially in settings where extensive genetic testing is not readily available. Chapter 3 addresses a fundamental challenge in medical imaging AI: accurate identification of MRI sequences. Recognizing the limitations of existing methods, this chapter proposes an innovative approach using large language models (LLMs) to parse MRI metadata for sequence identification. This method improved robustness to human errors in metadata entry and better generalization across institutions, potentially streamlining the preparation of large, multi-center datasets for AI model training. Finally, Chapter 4 provides a comprehensive discussion of the findings, their implications for clinical practice and research, and future directions for advancing the field of meningioma management through integrated imaging and genetic approaches. Throughout this thesis, we demonstrate the potential of combining advanced imaging techniques, genetic profiling, and artificial intelligence to enhance our understanding and management of meningiomas. By bridging the gap between radiological features and underlying genetic alterations, we aim to pave the way for more personalized and effective treatment strategies, ultimately improving outcomes for patients with these complex tumors.