This study investigates the factors influencing user perception and acceptance of electronic government services (e-government) in the context of technological advancements. The research integrates traditional and modern data analysis techniques, including Structural Equation Modelling (SEM), machine learning (ML), and multi-criteria decision-making (MCDM), to uncover interdependencies between variables from the perspective of online users. The developed models reveal underlying relationships in user attitudes towards e-government services. As customer satisfaction is subjective and dynamic, stakeholders should conduct regular measurements and data analysis to ensure continuous improvement of e-public services. The study aims to examine factors influencing user perception and intention to use e-public services. By establishing a comprehensive understanding of these factors, the research develops a theoretical framework and empirical models to guide government agencies in designing and implementing effective e-administrative systems. Additionally, it investigates the impact of demographic and socioeconomic factors, such as gender, age, education level, residence area, and monthly income, on user acceptance and adoption of e-administrative services. The main tasks of this research include proposing a methodological framework for systematic analysis of customer data, collecting and systemizing a customer dataset, creating and validating a Structural Equation Model (SEM), identifying key factors affecting customer use and intention to use e-administrative services, and creating and evaluating alternative ML and MCDA models for predicting user perception and adoption of e-administrative services. The study divides satisfaction factors into seven main groups and employs corresponding mathematical models for prediction. The obtained factors' weights can be integrated into multi-criteria assessment systems for evaluating e-administrative services. The main contribution of this paper is the development of a new complex methodology incorporating structural equation and ML models with MCDM for evaluation, comparison, and prediction of customer attitudes towards e-public services. The research provides an overview of e-government services and their assessment indicators, reviews relevant literature on user perception and acceptance of e-public services, outlines research objectives and methodology, presents results from the analysis of the collected dataset, and discusses the implications of the study, highlighting its contributions and future research directions in the field of e-administrative services.This study investigates the factors influencing user perception and acceptance of electronic government services (e-government) in the context of technological advancements. The research integrates traditional and modern data analysis techniques, including Structural Equation Modelling (SEM), machine learning (ML), and multi-criteria decision-making (MCDM), to uncover interdependencies between variables from the perspective of online users. The developed models reveal underlying relationships in user attitudes towards e-government services. As customer satisfaction is subjective and dynamic, stakeholders should conduct regular measurements and data analysis to ensure continuous improvement of e-public services. The study aims to examine factors influencing user perception and intention to use e-public services. By establishing a comprehensive understanding of these factors, the research develops a theoretical framework and empirical models to guide government agencies in designing and implementing effective e-administrative systems. Additionally, it investigates the impact of demographic and socioeconomic factors, such as gender, age, education level, residence area, and monthly income, on user acceptance and adoption of e-administrative services. The main tasks of this research include proposing a methodological framework for systematic analysis of customer data, collecting and systemizing a customer dataset, creating and validating a Structural Equation Model (SEM), identifying key factors affecting customer use and intention to use e-administrative services, and creating and evaluating alternative ML and MCDA models for predicting user perception and adoption of e-administrative services. The study divides satisfaction factors into seven main groups and employs corresponding mathematical models for prediction. The obtained factors' weights can be integrated into multi-criteria assessment systems for evaluating e-administrative services. The main contribution of this paper is the development of a new complex methodology incorporating structural equation and ML models with MCDM for evaluation, comparison, and prediction of customer attitudes towards e-public services. The research provides an overview of e-government services and their assessment indicators, reviews relevant literature on user perception and acceptance of e-public services, outlines research objectives and methodology, presents results from the analysis of the collected dataset, and discusses the implications of the study, highlighting its contributions and future research directions in the field of e-administrative services.