As smart cities continue to evolve, governments face unprecedented challenges in managing e-government systems. These challenges involve not only transparency and efficiency but also the ability to adapt to rapid technological changes. The present work explores the application of Markov chains as a methodological framework for modelling and optimising E-Governance in smart cities. By representing probabilistic transitions between different governance states, this approach provides predictive insights into the dynamics of transitions towards more efficient e-government systems. Incorporating key factors such as technological adoption, political innovation, and citizen participation, our approach simulates various transition scenarios and evaluates their impact on the quality and sustainability of public services. The simulation results reveal optimal pathways to enhance E-Governance, highlighting strategic levers that can ensure agile, sustainable, and inclusive governance systems. The present work makes a novel and substantial contribution to smart city research by proposing a decision-making framework based on stochastic modelling. It is particularly relevant to researchers, policymakers, and practitioners seeking to improve the effectiveness of public administration through the implementation of digital technologies. The findings offer concrete perspectives for designing governance strategies that are resilient and adaptable, accounting for the uncertainties inherent in technological advancements.