The Role of Hybrid Machine Learning and Policy in Advancing Big Data Applications

Abstract

The rapid integration of big data and artificial intelligence (AI) has transformed research and practice in diverse fields including healthcare, environmental science, and public policy. This narrative review examines emerging methods and applications, focusing on contextual factors, intervention strategies, and implementation barriers. Drawing from peer-reviewed literature published in the last five years, we conducted a structured search in Scopus, PubMed, and Google Scholar using targeted keywords and Boolean operators. Inclusion criteria prioritized methodologically sound studies with empirical contributions to the field. Findings reveal that infrastructure, policy environments, and data availability significantly influence the effectiveness of AI and big data. Successful interventions often involve hybrid machine learning models, institutional collaboration, and open data initiatives. However, systemic challenges—including limited infrastructure, regulatory ambiguity, and skill shortages—continue to impede implementation, especially in developing regions. Comparisons with high-income countries underscore the need for localized, adaptive strategies. This review suggests that effective integration of big data and AI requires supportive policies, ethical frameworks, and sustained investments in capacity-building. Future research should address regional disparities, enhance model transparency, and develop robust evaluation metrics. The insights offered herein can inform cross-sector policy, promote innovation, and guide sustainable, data-driven transformation across multiple domains.

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