In the context of intensifying climate change, this study explores how artificial intelligence (AI)–driven flood prediction accuracy and Internet of Things (IoT)–based environmental monitoring coverage contribute to disaster prevention effectiveness, with community readiness for technology adoption as a moderating factor. Grounded in Socio-Technical Systems (STS) Theory and the Technology Acceptance Model (TAM)/Unified Theory of Acceptance and Use of Technology (UTAUT), the study adopts a quantitative design with 385 valid responses from Vietnam, Singapore, and Malaysia, representing engineers, ICT managers, disaster officials, and community stakeholders. A structured 5-point Likert scale questionnaire was developed, to evaluate perceptions of predictive accuracy, monitoring coverage, disaster prevention effectiveness, and community readiness. Data analysis was conducted using SPSS, including reliability assessment (Cronbach’s alpha), exploratory factor analysis (EFA), linear regression, and moderation analysis with the PROCESS Macro to test the hypothesized relationships. Findings confirm that AI predictive accuracy enhances prevention not merely through numerical precision but by providing timely, actionable warnings. Likewise, IoT monitoring improves situational awareness, yet its value depends on strategic deployment, interoperability, and usability. Importantly, community readiness encompassing trust, literacy, affordability, and willingness to adopt emerges as the decisive factor that enables technological infrastructures to translate into protective action. The study advances theory by integrating socio-technical and adoption perspectives and offers practical insights, urging policymakers to invest not only in infrastructures but also in readiness-building, participatory engagement, and trust-enhancing initiatives.