This study employs a robust machine learning methodology to examine the intricate relationship between sustainable practices and the economic performance of startups through a comprehensive regression analysis. The study explores the extent to which sustainable practices influence the economic well-being of startups which addresses a critical gap in literature. The extra tree regressor method is used to conduct analysis keeping in view its prediction accuracy to handle complex datasets and accurate predictions. The findings reveal that SBP significantly impacts startups’ financial performance with some key emerging determinants highlighted in the study. The model demonstrates high resilience against overfitting and ensures the applicability in real-world scenarios, by elucidating the contributions of each variable toward financial performance prediction, the analysis equipped stakeholders with valuable information to prioritize strategic decision-making based on the triple bottom line. This study highlights the imperative for startups to embrace sustainability as an integral part of their operations not only for societal benefits but also for sustained financial viability and competitiveness in the entrepreneurial landscape.