Technology Business Incubation (TBIs) has become a global phenomenon integral to the growth of regional innovation and startup ecosystems. The availability of high-quality infrastructure and facilities lays the foundations of the entire startup ecosystem for providing essential support services that directly impact entrepreneurial success. The incubation capacity of TBIs across different regions can foster competition and collaboration among these regions, provide avenues for enhancing enterprises’ incubation capabilities, and assist entrepreneurs in assessing the strength of regional incubation. However, with their rapid expansion, the performance evaluation also becomes increasingly complex due to the diversity of converging factors such as complex technologies, varying nature of relationships of VCs, and entrepreneurial competencies of the founders incubating startups at the TBIs. Traditional Machine Learning performance evaluation and prediction models struggle to capture these dynamic variables, while also suffering from privacy vulnerabilities, low accuracy, and reliance on centralized third parties. This often leads to single points of failure, performance bottlenecks, and sometimes increased costs. To address these challenges, we employed Privacy-Preserving Federated Learning with Blockchain (PPFL-BC), a novel framework designed for improving the mechanism of performance measurement and prediction for remote TBIs while ensuring that the privacy of entities and the data remains secure. We utilize capabilities of Artificial Neural Network (ANN) and gradient boosting-enabled federated learning to train the model of each TBI locally. In the process, no private and sensitive business data is shared outside the network, significantly reducing the risk of privacy breaches. Besides this, all the locally trained models are aggregated into a unified predictive model at the central aggregation unit, which ultimately improves the overall accuracy of the performance prediction mechanism for the entire population of TBIs. In our model, the decentralized blockchain network is also used to address security concerns related to unauthorized access and data manipulation thereby ensuring transparent and tamper-proof model updates. We evaluate the performance of our proposed PPFL-BC model by utilizing real-world business incubation datasets. The simulation results show that our model outperforms the centralized performance prediction models in terms of accuracy, precision, recall, and F1-score. The results show that the proposed PPFL-BC model outperforms benchmark models with an accuracy of 84% and precision of 0.92, which shows the efficiency and reliability of our model in predicting and validating TBI success rates.