<p>Contemporary urban environments face increasing challenges related to climate change, socio-spatial inequality, and rapid technological transformation, placing growing pressure on conventional master planning approaches. This study advances Adaptive Master Plans (AMPs) as a computational planning framework designed to support iterative and data-informed planning processes. By integrating parametric modelling, geospatial analysis, and urban performance indicators, the research examines how alternative development scenarios can be evaluated within digital and data-driven planning environments. Three sequential case studies developed in Belo Horizonte, Brazil, illustrate distinct analytical dimensions of this approach. The first examines microclimatic impacts of densification strategies, the second analyses situated conditions of walkability and accessibility in peripheral centralities, and the third evaluates regenerative planning strategies through simulations of ecosystem services and environmental performance. Taken together, the results demonstrate how computational planning environments enable dynamic territorial diagnostics and predictive scenario evaluation. In this sense, Adaptive Master Plans expand the analytical and regulatory capacities of planning systems, supporting more adaptive and performance-oriented approaches to master planning while opening pathways for integration into institutional and regulatory frameworks.</p>