The paper presents a comprehensive review of the structural and dynamical organization of multilayer networks, which are graphs composed of multiple layers of interactions between nodes. It discusses the importance of considering multiple layers to accurately model complex systems, as traditional single-layer network models often fail to capture the nuances of real-world interactions. The paper covers various aspects of multilayer networks, including definitions, structural characteristics, generative models, resilience and percolation, spreading processes, synchronization, and applications in social, technical, economic, and biological systems. It highlights the need for a new framework to study multilayer networks, which can better represent the complexity of real-world systems. The paper also discusses different types of multilayer networks, such as multiplex, temporal, interacting, multidimensional, interdependent, multilevel, and hypernetworks. It emphasizes the importance of considering multiple layers to understand the dynamics of complex systems and provides a detailed analysis of various measures and models used to study these networks. The paper concludes with a discussion of the future directions and open questions in the field of multilayer network research.The paper presents a comprehensive review of the structural and dynamical organization of multilayer networks, which are graphs composed of multiple layers of interactions between nodes. It discusses the importance of considering multiple layers to accurately model complex systems, as traditional single-layer network models often fail to capture the nuances of real-world interactions. The paper covers various aspects of multilayer networks, including definitions, structural characteristics, generative models, resilience and percolation, spreading processes, synchronization, and applications in social, technical, economic, and biological systems. It highlights the need for a new framework to study multilayer networks, which can better represent the complexity of real-world systems. The paper also discusses different types of multilayer networks, such as multiplex, temporal, interacting, multidimensional, interdependent, multilevel, and hypernetworks. It emphasizes the importance of considering multiple layers to understand the dynamics of complex systems and provides a detailed analysis of various measures and models used to study these networks. The paper concludes with a discussion of the future directions and open questions in the field of multilayer network research.