Digital participation has transformed how communities engage in urban decision-making, generating vast amounts of citizen-generated data. Despite the potential for inclusive governance, the scale and unstructured nature of these inputs pose substantial analytical challenges. Traditional qualitative and quantitative methods often prove inadequate for managing large textual datasets, leading to incomplete or biased interpretations of public discourse. Emerging AI-driven techniques, such as topic modeling, natural language processing, and large language models, address this complexity by preserving contextual depth. However, interdisciplinary barriers, concerns about transparency, and oversimplification limit the effective incorporation of these techniques into post-participation. This chapter introduces a framework for democratizing advanced AI methods, bridging the divide between computational efficiency and qualitative depth. Emphasizing the user-centric design of a digital participation data analysis toolkit, the chapter demonstrates how AI-driven insights can be made accessible to stakeholders with diverse disciplinary backgrounds. The framework supports structured data preprocessing, multi-layered visualization, and iterative loops, ensuring that citizen inputs inform evidence-based decision-making. Drawing on three contrasting urban contexts, Singapore, Madrid, and Hamburg, this chapter illustrates the transformative impact of inclusive AI analytics on real-world governance and presents how responsible integration of AI methods can reinforce digital participation, linking complex data analysis with practical urban applications.