Decision Support Systems (DSS) are essential tools for managing complex decision-making processes in real-world scenarios. These systems support decision-makers in semi-structured and unstructured problems, where traditional algorithms are insufficient. DSS aims to enhance decision-making by providing quantitative support, complementing human intuition and experience. Key characteristics of DSS include data and model orientation, interactivity, and the inclusion of an intelligent knowledge base to quantify both technical and subjective constraints. The development of DSS has evolved significantly over the past few decades, driven by advancements in technology and the increasing complexity of decision-making problems. Early DSS focused on supporting managerial decisions through data analysis and modeling, while later developments incorporated artificial intelligence and expert systems. Today, DSS are integrated with business intelligence, data warehousing, and OLAP technologies, enabling more efficient and effective decision-making. DSS can be categorized into structured, semi-structured, and unstructured decisions based on the level of programming and the clarity of the problem. Structured decisions follow established procedures, while unstructured decisions require more judgment and adaptability. Multi-attribute decision-making (MADM) methods, such as the Maximin and Maximax methods, Pros and Cons analysis, and the Lexicographic method, are used to evaluate alternatives based on multiple criteria. Multi-Attribute Utility Theory (MAUT) and outranking methods like ELECTRE and PROMETHEE are also employed to handle complex decision-making scenarios. These methods allow for the evaluation of alternatives based on multiple criteria and provide a framework for comparing and ranking options. Sensitivity analysis is used to assess the impact of changes in input parameters on the final decision. This chapter provides an overview of DSS, its ideal characteristics, and various decision-making methods. It highlights the importance of DSS in supporting decision-makers in complex environments and the role of different techniques in enhancing the effectiveness and efficiency of decision-making processes.Decision Support Systems (DSS) are essential tools for managing complex decision-making processes in real-world scenarios. These systems support decision-makers in semi-structured and unstructured problems, where traditional algorithms are insufficient. DSS aims to enhance decision-making by providing quantitative support, complementing human intuition and experience. Key characteristics of DSS include data and model orientation, interactivity, and the inclusion of an intelligent knowledge base to quantify both technical and subjective constraints. The development of DSS has evolved significantly over the past few decades, driven by advancements in technology and the increasing complexity of decision-making problems. Early DSS focused on supporting managerial decisions through data analysis and modeling, while later developments incorporated artificial intelligence and expert systems. Today, DSS are integrated with business intelligence, data warehousing, and OLAP technologies, enabling more efficient and effective decision-making. DSS can be categorized into structured, semi-structured, and unstructured decisions based on the level of programming and the clarity of the problem. Structured decisions follow established procedures, while unstructured decisions require more judgment and adaptability. Multi-attribute decision-making (MADM) methods, such as the Maximin and Maximax methods, Pros and Cons analysis, and the Lexicographic method, are used to evaluate alternatives based on multiple criteria. Multi-Attribute Utility Theory (MAUT) and outranking methods like ELECTRE and PROMETHEE are also employed to handle complex decision-making scenarios. These methods allow for the evaluation of alternatives based on multiple criteria and provide a framework for comparing and ranking options. Sensitivity analysis is used to assess the impact of changes in input parameters on the final decision. This chapter provides an overview of DSS, its ideal characteristics, and various decision-making methods. It highlights the importance of DSS in supporting decision-makers in complex environments and the role of different techniques in enhancing the effectiveness and efficiency of decision-making processes.