This chapter investigates the key factors influencing post-IPO financial performance, with a focus on internal capability factors and the amount of funds raised, and evaluates the effectiveness of various predictive models in assessing firm success within the context of the Stock Exchange of Thailand (SET). Given the relatively small dataset, typical of an emerging and smaller market like the SET, the chapter compares traditional statistical methods, such as logistic regression, with machine learning techniques, including Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), and Artificial Neural Network (ANN). The analysis reveals that RF outperforms logistic regression and more advanced machine learning techniques regarding predictive power. RF performs particularly well in smaller datasets, where its simpler structure allows it to generalize better, while more complex algorithms may struggle with overfitting. Key factors such as funds raised, retention, and cost of goods sold are identified as the most significant contributors to post-IPO financial success prediction, with their relative importance shifting over time as firms mature. The chapter’s findings provide valuable insights for investors, firms, and stakeholders, particularly in emerging markets like Thailand.