The construction industry faces persistent challenges including cost volatility, labor shortages, project delays, safety risks, and regulatory complexity. This study analyzes the application of Artificial Intelligence (AI) across the construction lifecycle, from pre-construction estimation and financial forecasting to scheduling, workforce management, cybersecurity, and sustainable design. Using a structured analytical framework, the paper examines how machine learning, computer vision, predictive analytics, natural language processing, and generative design enhance decision-making, operational efficiency, and risk mitigation. The findings demonstrate that AI improves estimation accuracy, reduces delays, strengthens liquidity control, increases safety compliance, and supports sustainability objectives. Successful implementation, however, depends on robust data infrastructure and a human-in-the-loop model that integrates technological intelligence with professional expertise. A phased AI adoption roadmap is proposed to guide construction firms toward long-term resilience and competitive advantage.