To safeguard digital infrastructures from increasingly common and sophisticated cyberattacks, real-time detection is crucial. This paper introduces a methodology and platform for scalable attack detection that uses machine learning approaches to accurately and in real-time identify threats. System logs and network traffic are two examples of the heterogeneous data streams that the suggested system handles using advanced feature engineering methods. The ability to identify abnormalities and detect attack patterns in real-time is made possible by machine learning models like Autoencoders (AE), Gradient Boosting (GB), and Recurrent Neural Networks (RNN). Maintaining high detection accuracy while adjusting to emerging threats is made possible by the platform’s architecture, which supports constant learning. The model’s capacity to quickly and accurately identify different forms of attacks is shown by evaluations of datasets such as UNSW-NB15 and CICIDS2017. A trustworthy, efficient, and scalable real-time threat detection system is created by combining big data with machine learning. This solution takes on the challenges of modern cybersecurity. Tack detection is performed by training the RNN model on labeled datasets to recognize abnormal patterns. The model can be enhanced using hybrid deep-learning techniques and attention mechanisms. Scalability is achieved by deploying lightweight versions of the model on edge devices and using parallel processing for handling high-volume traffic efficiently.