This article is a review on the current developments in IoT security using cryptographic techniques and machine learning: 50 documents published between 2022 and 2024; It states that an IoT network gets larger. Therefore, such a property of networks makes them more vulnerable and, thus, demands scalable security solutions that are efficient and robust in providing security. Some hybrid cryptographic and machine learning approaches such as elliptic curve cryptography combined with anomaly detection have been found promising in improving the real-time detection of threats. It is in this respect that efficiency, scalability, and interpretability are yet to be defined. In this review, the need for standardized testing frameworks, privacy-preserving approaches, and adaptive models to accommodate different IoT contexts is indicated. The gaps include a lack of real-world use cases and associated benchmarking metrics for malware detection in addition to modeling trade-offs between security and computational efficiency. Future work focuses on developing scalable, interpretable, and privacy-preserving models that translate from theory into practice and further develop hybrid cryptographic and machine learning techniques for IoT security.