This paper explores the integration of data-driven decision-making strategies into IT project management to enhance sustainability and excellence. The authors propose a novel approach that leverages data analytics to optimize resource allocation, mitigate risks, and streamline project workflows. By aligning IT project management practices with business sustainability objectives, organizations can improve project success rates while minimizing environmental impacts and resource consumption. The research utilizes IT software project performance metrics and historical data, employing advanced analytics techniques such as multiple linear regression to analyze the impact of data-driven strategies on key dimensions of IT project management excellence. The study includes a sample of 350 small- and medium-sized enterprises in Serbia, providing valuable insights into the practical implications of data-driven approaches for sustainable IT project management. The findings confirm the effectiveness of predictive analytics for risk management and performance analytics for continuous improvement in enhancing resource utilization and return on investment. The research highlights the importance of efficient resource allocation and aligns with existing literature on sustainable project management and data-driven decision-making. Future research directions include exploring the integration of emerging technologies and conducting longitudinal studies to further validate the findings.This paper explores the integration of data-driven decision-making strategies into IT project management to enhance sustainability and excellence. The authors propose a novel approach that leverages data analytics to optimize resource allocation, mitigate risks, and streamline project workflows. By aligning IT project management practices with business sustainability objectives, organizations can improve project success rates while minimizing environmental impacts and resource consumption. The research utilizes IT software project performance metrics and historical data, employing advanced analytics techniques such as multiple linear regression to analyze the impact of data-driven strategies on key dimensions of IT project management excellence. The study includes a sample of 350 small- and medium-sized enterprises in Serbia, providing valuable insights into the practical implications of data-driven approaches for sustainable IT project management. The findings confirm the effectiveness of predictive analytics for risk management and performance analytics for continuous improvement in enhancing resource utilization and return on investment. The research highlights the importance of efficient resource allocation and aligns with existing literature on sustainable project management and data-driven decision-making. Future research directions include exploring the integration of emerging technologies and conducting longitudinal studies to further validate the findings.