Urban waste management in metropolitan cities such as Mashhad, due to population growth, changes in consumption patterns, and the increasing volume of waste, requires data-driven and transformation-oriented approaches based on the circular economy. The main objective of this study is to conduct a data-driven analysis and ranking of the indicators influencing the improvement of circular economy performance in Mashhad’s urban waste management system and to propose a practical model to support urban decision-making. To this end, the causal relationship structure among the indicators was first extracted using Fuzzy Cognitive Mapping (FCM). Then, the weights obtained from this model were combined with real data from the Mashhad Municipality Waste Management Organization covering the period from 2018 to 2024. Subsequently, a hybrid machine learning–based algorithm was developed by integrating three criteria: fuzzy weights of the indicators, correlation coefficients, and scatter scores, which enabled a multidimensional evaluation and accurate ranking of the indicators. The results indicate that the effectiveness of the circular economy in Mashhad’s urban waste management is primarily influenced by economic–institutional factors, the level of development of technical infrastructure, costs and revenues related to waste management, and the strengthening of intermediate production and consumption loops. Moreover, the roles of managerial policies, private sector investment, and the establishment of environmental management systems were highlighted as key drivers for improving the recycling rate. The proposed final model provides a practical framework for identifying strategic priorities and guiding managerial decisions. It can serve as a basis for formulating urban policies, prioritizing investments, and designing performance improvement programs for the Mashhad Municipality Waste Management Organization toward achieving a circular economy.