DOI: https://doi.org/10.36347/sjet.2025.v13i12.006

Pages: 915-930

Modern discovery is increasingly shaped by an integrated AI–Data–Compute–Governance stack that spans algorithms, software ecosystems, distributed infrastructure, cyber-physical systems, and socio-technical oversight. This review offers a pan-disciplinary synthesis across ten pillars Python/data-science ecosystems; ML/AI foundations including multimodality and RAG/agents; big data and the compute continuum (cloud–edge–HPC/quantum); IoT, robotics, and digital twins; bio/health informatics; geospatial/remote sensing; cybersecurity and privacy; blockchain for provenance; responsible governance; and sustainability/cost. We articulate a unifying lifecycle (design → train → evaluate → deploy → monitor → govern) and map cross-field patterns that consistently determine success: data quality over model size; retrieval-first, knowledge-integrated pipelines; agentic orchestration with strong evaluation; systems-level efficiency (compilers, quantization, distillation); privacy-by-design; and end-to-end assurance for safety, security, and robustness. Methodologically, we consolidate peer-reviewed literature and standards (2015–2025) from major digital libraries, structured via a transparent selection and evidence-grading protocol. The article contributes: (i) a taxonomy aligning methods and systems across scientific domains; (ii) reference blueprints for multimodal RAG/agent pipelines and edge-to-cloud deployment; (iii) comparative tables of tools, datasets, and evaluation suites; (iv) a measurement playbook spanning accuracy, reliability, security, privacy, latency, and energy/carbon; and (v) a 2025–2030 research roadmap prioritizing interpretable multi-agent systems, knowledge-grounded foundation models, privacy-preserving retrieval, green training/serving, and governance-aligned operations. By integrating perspectives from computer science, IT, data/AI engineering, and domain sciences, this review provides a coherent guide for researchers, practitioners, and policymakers seeki