K Lakshmaiah , S Mohan Krishna , P DharmaTeja , N Charan Kumar , S Ganesh Kumar 1 2 (April - June) Fraud detection holds significant importance in ecommerce transactions as it serves unauthorized activities like identity theft account takeovers and fraudulent transactions Recently machine learning algorithms have gained widespread adoption for fraud detection in ecommerce transactions These algorithms function by discerning patterns within the data indicative of fraudulent behavior Pattern detection entails identifying discriminative features in the data such as irregular transaction amounts locations or behaviors deviating from a users norm by inputting into the machine learning model In this research four fundamental machine learning algorithms decision tree random forest voting classifier are employed for fraud detection in ecommerce transactions utilizing a freshly curated dataset containing diverse features related to online shopping activities on Boyner Groups ecommerce platform and mobile app This study contributes to the existing literature by experimenting with various machine learning classifiers and incorporating distinct features diverging from prevailing methodologies in the field Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026