Abstract: Background: With the massive growth in e-commerce platforms, fraud detection systems have emerged as the need of today'stime. Therefore, promising solutions that can be provided by AI and ML technologies are the ones that can tackle security-related issuessuch as fraud detection, bot attacks, behavioral anomalies, and malware threats in the realm of an e-commerce platform. The current studyexplores the idea of how AI-driven fraud detection systems can be used to enhance the security of an e-commerce platform.Research Objectives: Primary aims of this research include studying how AI and ML algorithms can be applied to boost the detection offraud on e-commerce platforms, testing the efficacy of AI in limiting bot attack progression, and analyzing behavioral anomalies forimprovements in platform security. Furthermore, it will determine if AI-based malware detection works in averting security breaches. Inaddition, its performance in AI-based fraud detection measures on customers' trust and overall security of the platform will also beevaluated.Research Methodology: In the study, the descriptive and correlational research design was utilized when finding the effectiveness of AItechnologies in fraud detection and platform security. The sample for the study consists of 200 e-commerce platforms actively employingAI for the detection of fraud and implementing cybersecurity measures. The research is based on the use of structured questionnairesregarding the data-gathering process, which are administered to IT managers, cybersecurity experts, fraud detection analysts, and otherrelevant personnel.Findings: The study was able to discover that AI-based fraud detection highly improves accuracy in fraud detection. Machine learningalgorithms are able to explain 98.5% variance in the fraud-detection accuracy. Mitigation techniques for bot attacks that occur have a goodnegative correlation with the frequency of automated fraud and reduce fraudulent incidents as effectiveness increases. Behavioral anomalydetection also results in a positive impact on the identification of fraud activities, accounting for 90.2% variance in fraud-detection rates.Conclusion: AI-based technologies such as machine learning, bot attack mitigation, and anomaly detection play significant roles inimproving fraud detection while also aiding against cybersecurity breaches, thereby making the given e-commerce platforms more customertrustworthy.Keywords: AI-driven fraud detection, E-commerce platforms, Machine learning (ML), Cybersecurity, Fraud prevention