The increasing complexity of academic research projects, combined with constraints related to time, resources, and accountability, has intensified the need for more effective planning and control mechanisms. In this context, Artificial Intelligence (AI) has emerged as a promising tool to enhance project management practices by enabling data-driven decision-making, predictive analysis, and adaptive control. This study examines the application of AI-based smart planning and control strategies in academic research projects, with the aim of assessing their impact on project performance and identifying key implementation challenges. A quantitative, non-experimental and comparative research design was adopted, using a dataset composed of realistic simulated projects grounded in parameters reported in recent literature. Projects managed using traditional approaches were compared with those supported by AI-based strategies across indicators such as schedule deviation, resource efficiency, and risk incidence. The results show that AI-supported projects exhibit significantly lower schedule deviations, higher efficiency in resource utilization, and reduced occurrence of materialized risks. These findings highlight the potential of AI to improve the effectiveness and resilience of research project management in academic settings, while also underscoring the importance of addressing organizational, technical, and ethical challenges to ensure responsible and sustainable adoption.