The Internet of Things (IoT) has emerged as a transformative technology, connecting billions of devices worldwide. Efficient routing protocols and advanced machine learning techniques are crucial for ensuring seamless data transmission, energy efficiency, and robust security in IoT networks. This review paper synthesizes recent advancements in IoT routing protocols, with a focus on multi-objective optimization algorithms, context-aware routing strategies, and hybrid evolutionary techniques. It explores innovative approaches such as the Multi-objective Fractional Gravitational Search Algorithm (MoFGSA), Fractional Gravitational Grey Wolf Optimization (FG-GWO), and Whale Optimization Algorithms (WOA) for enhancing routing performance. Furthermore, the integration of machine learning models in IoT applications is discussed, particularly in phishing URL detection, cardiovascular disease prediction, and smart agriculture systems. The review highlights the growing role of deep learning frameworks, such as U-Net++, Mask-RCNN, and convolutional neural networks (CNN), in improving accuracy and decision-making capabilities within IoT ecosystems. Additionally, this paper examines the impact of blockchain technology on secure healthcare information exchange and discusses innovative approaches for low-cost smart devices in agriculture. By consolidating these advancements, this review identifies key trends, challenges, and future directions in optimizing IoT networks for enhanced scalability, security, and efficiency. This comprehensive analysis aims to guide researchers and practitioners in developing robust IoT frameworks that cater to diverse applications, including smart cities, healthcare, and industrial automation.