As multicriteria decision-making (MCDM) continues to grow and evolve, Machine Learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems. This book is intended to guide researchers, practitioners, and students interested in the intersection of ML and MCDM for optimal design. Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is a comprehensive resource that bridges the gap between ML and MCDM. It offers a practical approach by demonstrating the application of ML and MCDM algorithms to real-world problems. Through case studies and examples, it showcases the effectiveness of these techniques in optimal design. The book also provides a comparative analysis of conventional MCDM algorithms and Machine Learning techniques, enabling readers to make informed decisions about their use in different scenarios. This book is designed for professionals, researchers, and practitioners in engineering, Computer Science, sustainability, and related fields. It is also a valuable resource for students and academics who wish to expand their knowledge of Machine Learning applications in multicriteria decision-making.

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