Integration of Machine Learning Models to Predict and Prevent Security Breaches in IOD

The Internet of Drones (IoD) encounters major security issues that allow unauthorized users to breach systems while data breaches and cyberattacks threaten drone operation stability and all related sensitive information. Centralized security methods that exist today create vulnerabilities because they have one main failure point along with delayed responses to detected threats. A blockchain adaptation along with machine learning models powers the security approach delivered for IoD to anticipate and stop security breaches. The proposed system employs a Streamlit-based web interface for interactive data analysis and real-time decision-making. Based on operational drone parameters such as altitude, speed and battery level, direction and payload weight the framework determines drone security status as "Secured" or "Compromised". The research employs Naïve Bayes and Support Vector Machine (SVM) and Random Forest and Gaussian Naïve Bayes and XGBoost to create five machine learning models for evaluation. Experiment data shows Random Forest and XGBoost surpass the other models by reaching better threat prediction accuracy rates. To make the predictions more understandable confusion matrices together with feature importance visualizations are included. The application allows users to upload datasets followed by algorithm selection along with parameter input before it predicts drone security status to guarantee enhanced security and better threat mitigation and data integrity for IoD environments. An improved real-time security evaluation emerges from this work through a scalable system that relies on blockchain immutability and AI-based prediction capabilities.