The rapid growth of online platforms offering scholarships and internships has significantly benefited students. However, it has also led to a rise in fraudulent and spam opportunities that exploit applicants. These spam messages often contain misleading information, fake promises, and malicious links. This can cause financial loss and data theft. To address this issue, this research proposes a machine learning based scholarship and internship spam detection system that automatically classifies opportunities as legitimate or spam. The proposed system uses natural language processing techniques for text preprocessing. This includes tokenization, stop word removal, and feature extraction using Term Frequency Inverse Document Frequency TF IDF 2 , 3 . Multiple supervised machine learning algorithms, such as Naïve Bayes, Support Vector Machine SVM , and Logistic Regression, are trained and evaluated on a labeled dataset of genuine and spam scholarship and internship postings. Performance is measured using accuracy, precision, recall, and F1 score to identify the most effective model 4 . Experimental results show that the proposed system achieves high classification accuracy and effectively reduces student exposure to fraudulent opportunities. This system can be integrated into educational portals, email platforms, and job search websites to improve user safety and trust. With the abundant availability of online scholarships and internship opportunities, new opportunities for students development have arisen, but this phenomenon has also caused the rampant spread of deceptive and spam offerings. These scams look like genuine offers but trick students into divulging personal information, fees, or personal authentication. This paper introduces a machine learning based system that detects scholarship and internship spam and automatically classifies the postings as true or spam. The developed system first applies Natural Language Processing methods to clean the textual data such as normalization, tokenization, stop word removal, and stemming. It then employs Term Frequency Inverse Document Frequency TF IDF for feature extraction from the text, capturing important features. 2 , 3 Multiple supervised machine learning algorithms, such as Nave Bayes, Support Vector Machine and Logistic Regression, were trained on a dataset that had labeled scholarship and internship postings and assessed through the accuracy, precision, recall and F1 score. Experimental findings show that the suggested approach classifies the postings accurately and can effectively lower the false positive count, reducing the risk of students interacting with suspicious or malicious information. The system may be integrated into e learning websites, recruitment websites, and email services, providing a safer and more secure environment for students when searching for online career information 3 , 6 . Automated Detection System, Machine Learning ML , Spam Detection Scholarship, Spam Internship Fraud Detection, Student Protection System, Fake Opportunity Detection. 80-88 Advances in Computer Applications and Information Technology Special Issue Prajkta Burandea | Divya Bisenb