This article aims to determine factors crucial for the performance of new ventures by performing a meta-analysis. It evaluates the performance of machine learning and statistical models by selecting 19 studies through preferred reporting items for systematic reviews and meta-analyses (PRISMA). The results obtained were graphically represented using forest plots. A subjective analysis of the studies revealed business plan, market scope, team size, service timing, market growth, and age of the entrepreneur as crucial factors for deciding the performance of the new ventures. In machine learning model-based studies, the combined model was found to be statistically significant. The variables, namely, seed funding, funding rounds, location, social media presence, team size, number of founding members, and defunct date affect the predictive capability of these models. The implications of this article can be applied by start-ups to reduce uncertainty and enhance performance.