The adoption of Artificial Intelligence (AI) in the planning phase of construction projects in developing countries presents a transformative opportunity to enhance efficiency, reduce costs, and improve project outcomes. AI tools such as predictive modeling, reinforcement learning, pattern recognition, and deep learning enable accurate forecasting, adaptive decision-making, trend identification, and real-time collaboration among stakeholders. However, challenges such as inadequate infrastructure, limited technical expertise, and regulatory constraints hinder AI implementation. This study examines AI technologies used in the planning phase of construction projects in developing countries, focusing on their roles in project success. A quantitative research approach was adopted, employing structured questionnaires administered to 261 construction professionals in Ghana. Data analysis was conducted using Exploratory Factor Analysis (EFA) to identify key factors influencing AI adoption. Findings reveal the significant role of AI technologies, including predictive modeling, reinforcement learning, and pattern recognition, in enhancing data analysis and decision-making. Deep learning applications further improve efficiency, precision, and scalability in construction planning. These insights provide a foundation for future research on AI's impact in construction planning and highlight AI as a key enabler of efficiency and modernization in resource-constrained environments.