Recent research in cluster-based routing techniques for wireless sensor networks focuses on enhancing energy efficiency, scalability, and network lifetime through intelligent and adaptive clustering mechanisms. Modern approaches utilize fuzzy logic, machine learning, and optimization algorithms to improve cluster-head selection and dynamic cluster formation based on parameters such as residual energy, node density, and communication distance. Hybrid and trust-based clustering models are being developed to ensure balanced energy consumption, reduce routing overhead, and improve fault tolerance. These innovations aim to create context-aware and self-organizing routing protocols suitable for large-scale and heterogeneous WSN environments.