Recent research in cluster-based data aggregation techniques in wireless sensor networks (WSNs) focuses on improving energy efficiency, data accuracy, and network lifetime through optimized cluster formation and aggregation strategies. Advanced methods utilize machine learning, fuzzy logic, and swarm intelligence to dynamically select cluster heads and minimize redundant data transmission. Many studies also explore hierarchical and hybrid aggregation models that balance energy consumption across nodes while maintaining data integrity. These approaches enhance scalability and reliability, making them highly suitable for large-scale and real-time WSN applications such as environmental monitoring and smart cities.