Urbanization, along with climate change, has led to changes in landscapes and other environmental changes, resulting in heat waves and elevated temperatures, impacting health, the environment, and the economy. To manage these issues, reliable modeling frameworks, including numerical and machine learning models, are needed. Integrating machine learning techniques is a promising development in weather and climate prediction due to its satisfactory skills while requiring lesser computing resources. Machine learning enhances forecast accuracy by accounting non-linearity patterns in large sets of historical weather and climate data, providing an encouraging scientific approach to the challenges posed by climate change. Researchers are currently exploring data-driven models incorporating machine learning techniques to predict extremes like heatwaves, droughts, and rainfall. Machine learning techniques, such as regression, classification, deep learning, clustering, dimensionality reduction, anomaly detection, LSTM, ARIMA, and SARIMA, are used for heatwave studies. Notably, hybrid models, which combine physics-based numerical weather prediction (NWP) models with machine learning or statistical models, have shown their usefulness in predicting heat waves. The availability of an abundance of data and the frequency of diverse data formats presents distinctive opportunities for leveraging these technologies for data processing and analysis for heat wave research. Data-driven modeling plays a crucial role in advancing heat wave research, enabling a better understanding of heat wave dynamics and providing valuable information to mitigate heatwave impacts on human health, infrastructure, and ecosystems. Another vital point is that the effective coordination among public health representatives, city administrators, stakeholders, non-governmental organizations, scientists, academicians, and technology professionals is essential for compiling and disseminating information for heat wave preparedness plans, emergency response strategies, and urban resilience initiatives, emphasizing the significance of each stakeholder’s role in the coordinated effort.