The growing complexity of cyberattacks demands intelligent and adaptive security solutions, one of which is the detection of malicious IP addresses. This paper presents a novel approach for classifying IP addresses by integrating Machine Learning (ML) with data from multiple public threat intelligence databases, where a novel dataset was built. The proposed solution applies a weighted voting mechanism to enhance interpretability and robustness by combining diverse data sources through a multi-criteria weighting strategy. The experimental results, in a real network environment, indicate that the solution enables scalable and automated risk classification of IP addresses.