Machine Learning and Urban Health

Data-Driven Sustainable Urban Health for Smart Cities

explores how advanced data analytics and machine learning are reshaping the future of urban wellness.

As cities worldwide evolve into highly interconnected, technology-driven spaces, new opportunities—and challenges—emerge for ensuring sustainable urban health.

This timely volume addresses the critical question: how can we harness the power of data to improve health outcomes in complex urban environments?

Traditional models of urban health management often fall short in the face of modern socio-environmental pressures and rapid urbanization.

This book bridges that gap by presenting data science and artificial intelligence as essential tools for navigating the multifaceted nature of urban health.

From predicting disease outbreaks to identifying vulnerable populations and optimizing resource allocation, the book highlights how machine learning is transforming the public health landscape.

Drawing on real-world case studies and diverse datasets—including environmental sensors, population health indicators, and urban infrastructure metrics—the book provides actionable insights for researchers, policymakers, urban planners, and public health professionals.

It emphasizes not only technical innovation but also the ethical and equitable use of health data to reduce disparities and enhance quality of life for all city residents.

By integrating theory with application, Data-Driven Sustainable Urban Health for Smart Cities serves as both a foundational text and a visionary guide for building smarter, healthier, and more resilient urban communities in the 21st century.