N. Venu Sidhardha1, M. Amarnadh2, M. Karthik3, P. Anitha4 03 3(1) The widespread adoption of social media platforms has given rise to a parallel epidemic of fraudulent accounts designed to manipulate engagement metrics distribute misinformation and conduct phishing operations at scale This paper proposes a machine learningbased system for automatically detecting fake Instagram profiles by analyzing behavioral and profilelevel features extracted from labeled account datasets The proposed framework processes structured attributesincluding follower and following counts posting frequency engagement ratios and profile completeness indicatorsthrough a preprocessing pipeline that handles missing values normalizes numerical features and engineers derived attributes such as the followertofollowing ratio Classification is performed using a Random Forest ensemble model and a multilayer Neural Network trained with the Adam optimizer and categorical crossentropy loss Model evaluation on heldout test data demonstrates classification accuracy exceeding 90 with strong precision and recall scores across both genuine and fraudulent classes The system further integrates an OpenCVbased face detection module and an EasyOCR text extraction pipeline to analyze profile images enabling richer feature construction A webbased dashboard provides realtime prediction visualization of results and an intuitive interface for analysts Results confirm that the combined use of behavioral feature engineering and neural classification substantially outperforms traditional rulebased detection mechanisms in identifying sophisticated fake accounts Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026