## 4 Discussions
Methodologically, the literature shows a move away from reliance on single machine learning algorithms toward more comprehensive, hybridized approaches that blend AI with digital-twin frameworks, IoT sensor webs, advanced geo-statistics, and multi-scale remote-sensing platforms. Such combinations not only improve predictive performance but also create operational feedback loops that can be actively steered toward sustainability outcomes. The social dimension of AI applications likewise demonstrates positive momentum. Intelligent information systems facilitate citizen engagement in environmental governance by lowering the transaction costs of public participation. Open-access dashboards that visualize air-quality data or watershed health enable informed dialogue between civil-society groups and regulatory agencies, strengthening procedural justice in environmental decision making. Importantly, the literature shows that these technologies yield disproportionate benefits in regions that historically face deficits in institutional capacity, suggesting a democratizing potential when ethical safeguards and digital-literacy programmers accompany deployment. Notwithstanding these encouraging developments, systemic challenges continue to constrain large-scale implementation. Digital inequality remains one of the most pressing barriers. Both cross-national and intra-national disparities in broadband access, high-performance computing resources, and data-science expertise hamper the diffusion of AI-based solutions, especially in rural and suburban settings [118]. Without concerted investment in digital infrastructure and human capital, AI may widen rather than narrow existing socio-economic divides. Institutional and regulatory obstacles further complicate the landscape. The absence of coherent standards governing algorithmic transparency, data-protection protocols, and model interpretability is particularly problematic in safety-critical contexts such as health care, flood-risk management, and critical-infrastructure control [160]. Fragmented regulatory regimes not only heighten legal uncertainty for innovators but also erode public trust, which is essential for sustained technology uptake. Compounding this problem is the so-called black-box nature of many high-performance AI models; their internal logic is often opaque even to domain experts, limiting their suitability for public-sector decision making and undermining democratic accountability [46; 73]. Recent practice points to several oversight mechanisms--independent algorithm audits, federated data spaces that preserve ownership rights, and public registers of high-impact models--that offer pragmatic pathways for aligning AI innovation with accountability and equity goals. Limitations intrinsic to the present review must also be acknowledged when interpreting these findings. A substantial proportion of the analyzed literature focuses on case studies from Asia, whereas Africa and Europe remain comparatively under-studied; this regional concentration introduces a geographical bias that limits the generalizability of conclusions, particularly in view of the distinctive agro-ecological and infrastructural characteristics that prevail in North and Latin America. Moreover, the review did not identify eligible peer-reviewed studies from Australia or New Zealand, which may reflect either a true research gap or limitations inherent to the database selection and search strategy. Furthermore, the corpus is heavily skewed toward technical research questions, while institutional, legal, and sociocultural dimensions of AI adoption are markedly underexplored. This imbalance restricts a holistic understanding of the enabling and constraining factors that determine success in specific locales. This review also relies almost entirely on peer-reviewed literature, thereby under-representing the practical insights embedded in grey literature such as policy briefs and implementation reports.