Construction projects on highways contain complications, including many stakeholders, environmental considerations, and technological difficulties. Minimizing adverse effects and guaranteeing project success depends on effective risk management. The current practices in the construction sector and the science cannot frame the affiliation between the project work and its features, the project aspects of uncertainty and complexity of the risk profile, and its requisite response strategy. A questionnaire survey is conducted to assess the risk factors that have a significant influence on highway construction projects in India are analyzed, and these factors are analyzed using the statistical method to develop a risk response strategy based on the Relative Importance Index and to provide suggestive measures that can improve the overall highway construction works. Two case studies are provided to investigate the risk management techniques applied in actual situations. Descriptive statistics is utilized to determine prevalent risks and their potential outcomes, effects, and mitigation techniques. In addition, an Artificial Neural Network (ANN) model is created to evaluate and forecast possible risks throughout the project. The results underscore the need to employ proactive risk identification and mitigation strategies to improve highway construction projects’ overall resilience and performance.