This study investigates the factors influencing user perception and acceptance of electronic government services (e-government services) in the context of technological advancements. The research focuses on classifying the main features of e-administrative systems, emphasizing user satisfaction by integrating traditional and modern data analysis techniques. Structural Equation Modelling (SEM), machine learning (ML) techniques, and multi-criteria decision-making (MCDM) methods are applied to survey data to uncover interdependencies between variables from the perspective of online users. The developed models aim to explain the 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. electronic public services; electronic government services; technology adoption; customer satisfaction; behaviour intention; structural equation modelling; PLS-SEM; machine learning Electronic administrative processes streamline traditional operations, reducing costs and contributing to a more efficient, transparent, and customer-centric administrative ecosystem. Digital government platforms enhance connectivity and improve the quality and accessibility of public services. The digital evolution of public administration supports the transition to cleaner energy sources and positively influences societal progress. E-government refers to the use of information and communication technologies (ICT) in delivering public services, ensuring dynamic interactions even during crises. The COVID-19 pandemic accelerated the dissemination of e-government services, but there has been a decrease in requests for e-administrative services as health situations normalize. Europe has consistently held the highest average Electronic Government Evaluation Index (EGDI) among continents, with significant variations among European countries in key indicators such as transparency and cross-border services. New e-administrative services often face challenges beyond software implementation, such as integration, user awareness, and training needs. The study aims to examine factors influencing user perception and intention to use e-public services, develop a theoretical framework and empirical models, and investigate the impact of demographic and socioeconomic factors on user acceptance and adoption. The main tasks include proposing a methodological framework, collecting customer data, creating and validating a Structural Equation Model (SEM), identifying key factors affecting user use and intention, and creating and evaluating alternative ML and MCDA models for prediction. The study contributes to the development of a new complex methodology incorporating structural equation and ML models with MCDM for evaluating, comparing, and predicting customer attitudes towards e-public services.This study investigates the factors influencing user perception and acceptance of electronic government services (e-government services) in the context of technological advancements. The research focuses on classifying the main features of e-administrative systems, emphasizing user satisfaction by integrating traditional and modern data analysis techniques. Structural Equation Modelling (SEM), machine learning (ML) techniques, and multi-criteria decision-making (MCDM) methods are applied to survey data to uncover interdependencies between variables from the perspective of online users. The developed models aim to explain the 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. electronic public services; electronic government services; technology adoption; customer satisfaction; behaviour intention; structural equation modelling; PLS-SEM; machine learning Electronic administrative processes streamline traditional operations, reducing costs and contributing to a more efficient, transparent, and customer-centric administrative ecosystem. Digital government platforms enhance connectivity and improve the quality and accessibility of public services. The digital evolution of public administration supports the transition to cleaner energy sources and positively influences societal progress. E-government refers to the use of information and communication technologies (ICT) in delivering public services, ensuring dynamic interactions even during crises. The COVID-19 pandemic accelerated the dissemination of e-government services, but there has been a decrease in requests for e-administrative services as health situations normalize. Europe has consistently held the highest average Electronic Government Evaluation Index (EGDI) among continents, with significant variations among European countries in key indicators such as transparency and cross-border services. New e-administrative services often face challenges beyond software implementation, such as integration, user awareness, and training needs. The study aims to examine factors influencing user perception and intention to use e-public services, develop a theoretical framework and empirical models, and investigate the impact of demographic and socioeconomic factors on user acceptance and adoption. The main tasks include proposing a methodological framework, collecting customer data, creating and validating a Structural Equation Model (SEM), identifying key factors affecting user use and intention, and creating and evaluating alternative ML and MCDA models for prediction. The study contributes to the development of a new complex methodology incorporating structural equation and ML models with MCDM for evaluating, comparing, and predicting customer attitudes towards e-public services.