The growth of mobile applications has reached extraordinary levels because they provide users better features and convenience for daily needs. Massive mobile application expansion resulted in fresh security issues creating an increased number of cyber threats throughout the market. The number of cyber risks increases in Germany since malicious applications both harm device integrity and illegally obtain user information to threaten individual privacy together with organizational security measures. The development of strong automated security systems with locating and removal capabilities of threats remains an urgent matter. Cybersecurity systems at present base their protection on fixed rule systems alongside traditional machine learning approaches. The implemented security methods deliver protection however they demonstrate reduced performance during changes in cyber threat patterns. The system exposes several entry points which hackers can leverage for attack purposes. An innovative cybersecurity framework has been developed which incorporates the RF algorithm with PCA through this project to address existing limitations. The system uses mobile application metadata preprocessing to accomplish three tasks through PCA: elimination of unneeded data and retention of important features along with feature selection. This procedure simultaneously maintains data authenticity as well as enhances processing speed. Random Forest becomes operational to classify mobile applications between malicious and safe categories within the framework. RF algorithm and PCA form a flexible and scalable system which enables handling extensive datasets along with security adaptation towards the latest threats. The system has a user-friendly interface which enables users to enter mobile app metadata and get prompt predictions along with performance scores. Mobile app users benefit from predictions which help them decide about the safety of apps through confidence scoring. The system maintains strict validation procedures across unidentified datasets which confirm its dependable and practical functionality during actual use. Evaluation of the model effectiveness depends on performance metrics that include accuracy together with precision, recall and F1-score. The system delivers complete performance information which allows end-users to receive practical insights and enable greater transparency.