1Professor, School of Liberal Arts and Management, P P Savani University, Dhamdod, Kosamba, Surat, 394125, Gujarat, India
Email: aparnavajpee@gmail.com
Mobile: +91 9123647912
ORCID: https://orcid.org/0000-0003-4616-8194
2Research Scholar, School of Liberal Arts and Management, P P Savani University, Dhamdod, Kosamba, Surat, 394125, Gujarat, India
Email: abhilashasahayvarma@gmail.com
Mobile: +91 9031439111
ORCID: https://orcid.org/0009-0003-8778-4249
3Research Scholar, School of Liberal Arts and Management, P P Savani University, Dhamdod, Kosamba, Surat, 394125, Gujarat, India
ORCID: https://orcid.org/0009-0009-7020-6817
4Professor, University Institute of Media Studies, Chandigarh University, NH-95, Chandigarh-Ludhiana Highway, Gharuan, Mohali, Punjab - 140413, India
Mobile: +91 9038646824
Email: kaushikmishra28@gmail.com
ORCID: https://orcid.org/0000-0003-1396-045X
5Director/Professor: Management/Commerce/International Business, DR G R D College of Science, India
Email: dr.k.k.ramachandran@gmail.com
ORCID: https://orcid.org/0000-0003-0589-4448
6Associate Professor, Department of Management Science, Saveetha Engineering College, Thandalam, Chennai, India
Email: karthick.hr@gmail.com
ORCID: 0000-0002-9379-6794
Corresponding Author:
Aparna Vajpayee
Professor, School of Liberal Arts and Management, P P Savani University, Dhamdod, Kosamba, Surat, 394125, Gujarat, India
Email: aparnavajpee@gmail.com
ORCID: https://orcid.org/0000-0003-4616-8194
Public health systems around the world are facing increasing challenges due to rapid urbanization, emerging infectious diseases, aging populations, and limited healthcare resources. Traditional public health monitoring methods often rely on retrospective data collection and delayed reporting systems, which can limit the ability of health authorities to respond quickly to emerging health threats. In recent years, advances in artificial intelligence (AI), machine learning, and big data analytics have created new opportunities for transforming public health surveillance systems. AI-enabled predictive analytics allows healthcare institutions and government agencies to monitor population health trends in real time, identify potential disease outbreaks early, and design targeted interventions to improve community wellness. This study explores the role of AI-driven predictive models in enhancing public health monitoring systems and improving community-level healthcare outcomes. The proposed framework integrates multiple data sources including electronic health records, wearable health devices, environmental sensors, and social media analytics to develop a comprehensive AI-based health monitoring platform. By applying machine learning algorithms and predictive analytics techniques, the system can detect abnormal health patterns, forecast disease spread, and assist policymakers in making data-driven public health decisions. The research adopts a multidisciplinary approach that combines perspectives from public health informatics, data science, epidemiology, and healthcare management. Empirical analysis demonstrates that AI-enabled monitoring systems can significantly improve early detection of health risks, enhance disease prevention strategies, and optimize resource allocation within healthcare systems. The study also highlights important challenges related to data privacy, algorithmic bias, and infrastructure limitations that must be addressed to ensure responsible and ethical implementation of AI in public health. Overall, the findings emphasize that integrating artificial intelligence into public health monitoring frameworks can strengthen healthcare systems, improve disease prevention capabilities, and promote sustainable community wellness in the digital health era.
Keywords: Artificial Intelligence, Public Health Monitoring, Predictive Analytics, Community Wellness, Health Informatics, Disease Surveillance, Digital Health Systems.
How to cite this article: Vajpayee A, Varma A, Bokey E, Mishra K, Ramachandran KK, Karthick KK. AI-Enabled Public Health Monitoring Enhancing Community Wellness Through Predictive Analytics. Int J Drug Deliv Technol. 2026;16(8s): 257-267; DOI: 10.25258/ijddt.16.8s.37
Source of support: Nil.
Conflict of interest: None