By pooling small contributions from a broad audience, crowdfunding platforms enable entrepreneurs and small businesses from developing countries to access critical funding that traditional financial systems may not provide. This study implements machine learning methods to detect success trends in Developing World Crowdfunding. Using data from crowdfunding websites, like Kiva.org, a leading microlending platform, we used machine learning to determine what makes projects successful. Our findings reveal several key predictors of campaign success, including the borrower's region, gender, loan amount, and sector of activity. Notably, the models demonstrated strong predictive power, highlighting clear patterns in how these factors influence funding outcomes. Beyond prediction, the study offers valuable insights for practitioners and platform designers aiming to improve campaign visibility and funding efficiency. For example, gender and regional disparities uncovered by the models point to structural imbalances that can be addressed through targeted support or algorithmic adjustments. By translating raw campaign data into actionable knowledge, this research contributes to developing more equitable and effective crowdfunding ecosystems.