One of the most important requirements of the information age is the ability to produce systematic and data-driven solutions to complex problems. Decision-making processes are of strategic importance at every level, from individuals to societies and even states. In today's world, decisions must be made under constantly changing conditions, and they must be not only correct but also timely, multidimensional, and dynamic. In this regard, quantitative decision-making approaches, particularly Multi-Criteria Decision Making (MCDM) and Machine Learning methods, offer decision-makers a powerful roadmap. This book aims to contribute to the field by bringing together the theoretical foundations and current applications of these two powerful methodologies. Shaped by the contributions of valuable researchers from different disciplines, this compilation offers researchers seeking to understand and improve the decision-making process a unique interdisciplinary perspective.

Comprising a total of eight chapters, the book takes readers on a comprehensive journey from theory to application. The first chapter introduces the WENSLO criterion weighting and CoCoSo ranking methods from the MCDM methods with an application. The second chapter presents a bibliometric analysis of pioneering studies aimed at understanding the use of machine learning in portfolio studies. The third chapter demonstrates the combined use of ENTROPY and TOPSIS MCDM methods through an application comparing the health performance of EU candidate countries and Turkey. The fourth chapter analyses machine learning studies on customer satisfaction using a bibliometric approach. The fifth chapter explains the integration of MCDM and machine learning with a theoretical framework and application. The sixth chapter outlines a general framework for examining machine learning algorithms in financial time series forecasting. The seventh chapter demonstrates the application of current MCDM methods based on FUCOM WEDBA in optimal supplier selection. The eighth chapter presents an application on how to predict the likelihood of employee turnover using various machine learning algorithms.

I hope that this book will not only provide readers with the ability to systematically manage quantitative decision-making processes based on data using multi-criteria decision-making and machine learning approaches, but also serve as a guide. We would like to thank all the authors who contributed to the preparation of this book and everyone who worked on the process. We hope that this work will inspire readers and serve as a starting point for future research.

With the hope that the multi-voiced and interdisciplinary nature of science will continue to illuminate our path toward more equitable, effective, and sustainable decisions...

Editor

Dr. Şule BAYAZİT BEDİRHANOĞLU

May 2025