Wireless Sensor Networks (WSNs) are essential for real-time monitoring applications like industrial automation, healthcare, environmental surveillance, and defense; however, their widespread deployment is limited by energy resources, scalability problems, and security flaws. With an emphasis on MAC protocols, clustering mechanisms, hybrid network architectures, and AI/ML-based optimization techniques, this survey methodically examines current energy-efficient and secure communication strategies used in WSNs. Hybrid designs and learning-driven models greatly improve network lifetime, throughput, reliability, and latency performance, according to a comparative study of current methods. Despite these advancements, many solutions still exhibit high computational overhead, limited flexibility in dynamic environments, and insufficient real-world validation. Future WSN frameworks must prioritize lightweight, adaptive, and cross-layer intelligent designs to achieve sustainable, scalable, and secure next-generation sensor networks.

This survey thoroughly examined energy-efficient and secure communication strategies in Wireless Sensor Networks, emphasizing the transition from conventional protocol-based designs to advanced, AI/ML-integrated hybrid frameworks. Recent approaches show significant improvements in network lifetime, reliability, and latency, but they still have problems with high computational overhead, limited scalability, and insufficient real-world testing. Most current research relies on static assumptions and simulation-based evaluation, which makes it hard to apply in real life. Future research should look into lightweight, adaptable, and self-learning models that work with edge computing, blockchain-based trust management, and real-time energy harvesting systems to make next-generation WSNs that can grow and remain resilient.

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