<![CDATA[Tropical heritage buildings are increasingly exposed to complex environmental threats such as high humidity, air pollution, and temperature volatility, all of which accelerate material degradation and undermine their cultural and economic value. Traditional conservation methods—often reactive and periodic—have proven inadequate in addressing these compounded risks. This study aims to develop a smart, economically sustainable framework for heritage management by integrating Artificial Intelligence (AI) and the Internet of Things (IoT) technologies. Using Lawang Sewu, a historic colonial-era building in Semarang, Indonesia, as a case study, the research employs a mixed-method approach combining a systematic literature review, bibliometric analysis, and thematic synthesis to assess technological trends, risk categories, and implementation gaps in tropical contexts. A seven-phase adaptive model is proposed, incorporating real-time environmental monitoring, predictive analytics, Heritage Building Information Modeling (H-BIM), and Digital Twin technologies to support proactive decision-making, optimize maintenance cycles, and reduce long-term conservation costs. The results reveal that AI-IoT convergence not only enhances environmental responsiveness but also protects tourism-based revenue streams and aligns with Sustainable Development Goals (SDGs), particularly SDG 11 and SDG 8. The proposed strategy positions heritage preservation as a digitally enabled economic investment, transforming static monuments into dynamic, climate-resilient assets. This research contributes a replicable model for policymakers, urban planners, and conservation professionals seeking to bridge cultural heritage management with digital innovation and sustainable economic development in tropical urban regions.]]>
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