The rapid growth of financial technology and artificial intelligence has significantly transformed the traditional credit evaluation process across emerging economies. Artificial Intelligence (AI)–driven credit scoring systems are increasingly being adopted by financial institutions, digital lending platforms, and FinTech companies to improve the speed, efficiency, and accuracy of credit assessment. These intelligent systems utilize machine learning algorithms, big data analytics, behavioural data, mobile transaction records, and alternative financial indicators to evaluate the creditworthiness of individuals who are often excluded from conventional banking systems. Emerging economies, where a large proportion of the population remains underbanked or financially underserved, present a strong environment for the implementation of AI-based credit scoring mechanisms. The main aim of the researchers is to evaluate the effectiveness of Artificial Intelligence–driven credit scoring systems in improving predictive accuracy, promoting financial inclusion, and addressing ethical challenges within emerging economies. The study further seeks to examine how AI-enabled credit assessment models influence access to financial services, lending decisions, transparency, bias reduction, and customer trust in digital financial environments. The study adopts an analytical and empirical approach by examining existing AI credit scoring practices, investor and consumer perceptions, financial institution strategies, and ethical concerns associated with algorithmic decision-making. The research highlights that AI-driven systems enhance operational efficiency, reduce loan processing time, and improve risk prediction capabilities when compared to traditional credit evaluation methods. Additionally, these technologies support financial inclusion by enabling access to credit for individuals lacking formal credit histories through the use of alternative digital data sources. However, the study also identifies significant ethical and regulatory challenges, including algorithmic bias, data privacy concerns, lack of transparency, digital discrimination, and overdependence on automated decision-making systems. The findings emphasize that while AI-driven credit scoring systems offer substantial opportunities for inclusive financial growth, responsible implementation, regulatory monitoring, ethical governance, and human oversight are essential to ensure fairness, accountability, and long-term sustainability in financial services. The primary aim of the researchers in their research is to critically evaluate the effectiveness of Artificial Intelligence–driven credit scoring systems in emerging economies by examining their predictive accuracy, contribution toward financial inclusion, and associated ethical challenges.