In this paper, the author is aimed at presenting how machine learning enhances detection of cyber threats with appropriate feature engineering and quality datasets. The author employs commonly used benchmarking datasets such as KDDCup99, NSL-KDD, and CICIDS2017 that provide a good platform for intrusion detection system training and evaluation. Other attributes such as IP address, type of protocol, and other network flow attributes are essential to learning and improve threatdetectionaccuracy. However, the writer identifies severe issues in this space, including the issue of unbalance within datasets where traffic is benign-centric, and advanced evasions being used to make evasion from detection systems. They are constraints which bring down performance in conventional machine learning models and call for devising more resilient and adaptive models. To answer all of this, the author feels it is feasible to discuss sophisticated techniques such as federated learning, where model training could be conducted on a decentralized platform while data are secured. some of which are being positively developed include threat response automation and feature extraction, leveraging secure logging mechanisms through the implementation of blockchain technology, and adaptive models having the ability to learn how to adapt to future threats. The accuracy and flexibility of cyber threat detection systems can be significantly enhanced by following these steps. The author wishes that this work contributes to the pool of research that attempts to enhance cybersecurity mechanisms by utilizing machine learning-based techniques that are smart, scalable, and secure.