Mobile health (mHealth) systems in rural communities require rigorous software engineering methodology and empirical validation of end-user acceptance. A gap exists in applying structured System Development Life Cycle (SDLC) frameworks to community-facing mHealth platforms with embedded technology acceptance evaluation. This study presents the design, architecture, and iterative development of the “Smart Daily Life Care” cross-platform mobile application using a six-phase SDLC framework, targeting rural elderly communities in Thailand. The system architecture employed a microservices design with age-friendly UI engineering, conforming to WCAG 2.1 AA. Technology acceptance was evaluated post-deployment using the Technology Acceptance Model (TAM) with 200 participants (elderly users, caregivers, and health personnel). System efficiency was rated at $\overline{\mathrm{x}}$ = 4.58 and user satisfaction at $\overline{\mathrm{x}}$ = 4.64. TAM regression identified perceived usefulness as the dominant predictor of behavioral intention (β = 0.412), followed by perceived ease of use (β = 0.318) and social influence (β = 0.268), with R² = 0.682. Integrating TAM evaluation within SDLC phases enables iterative remediation of acceptance barriers before deployment. Village Health Volunteer networks function as indispensable sociotechnical enablers of adoption. The SDLC–TAM integration provides a structured methodological approach suitable for replication in age-sensitive health information systems in low-resource settings.

This study explores the interconnections between new farming practices, smart agricultural technology, mathematical skills, data-driven decision-making, and community learning in areas commonly affected by drought. Using a statistical method known as Structural Equation Modeling (SEM) and data from 320 farmers, the study explores how new farming ideas encourage smart practices that improve math skills. It also demonstrates how smart farming creates an environment where data helps inform decision-making, which benefits community learning. The results indicate that New Theory Agriculture (NT) encourages Smart Agriculture (SA) engagement, thereby facilitating both Mathematical Literacy (ML) and Data-Driven Decision-Making (DD). Engagement in SA is closely linked to improvements in ML, which, in turn, strengthen DD abilities. ML plays a central role by serving as a bridge between SA and DD, which, in turn, directly affects Community Learning Outcomes (CL). The findings show that NT fosters community-level outcomes by first building SA and ML, both of which shape DD and ultimately enhance CL, clarifying the sequence of concept connections. The findings reveal that implementing NT and smart technology in agriculture systematically enhances farmers’ resource management and the evolution of mathematical and data skills beyond formal education. The research demonstrates how cognitive skills, technological participation, and collective learning are linked within the community: NT leads to SA engagement, which develops ML, enables DD, and produces CL. The study discusses implications for community education, digital agriculture policy, and rural capacity development, suggesting that future longitudinal or experimental studies could clarify how these connections change over time.