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Wireless Sensor Networks (WSNs) have emerged as a fundamental technology for a wide range of applications, including environmental monitoring, smart agriculture, healthcare, and industrial automation. However, the limited energy resources of sensor nodes and the high cost of data communication pose significant challenges to network efficiency and lifetime. In recent years, prediction-driven communication models have gained considerable attention as an effective approach to reduce unnecessary data transmissions by exploiting temporal and spatial correlations in sensed data. These models enable sensor nodes to locally predict future readings and transmit data only when the prediction error exceeds a predefined threshold, thereby minimizing communication overhead. This survey provides a comprehensive review of prediction-based communication techniques in WSNs, covering model-based, threshold-driven, and machine-learning (ML) based approaches. It also examines how these techniques are integrated with routing strategies, particularly multi-hop communication, to further enhance energy efficiency. The paper analyses existing methods in terms of energy consumption, accuracy, computational complexity, and network scalability. Additionally, key challenges, limitations, and trade-offs associated with prediction-driven communication are discussed. Finally, the survey highlights open research issues and future directions for developing more robust and adaptive prediction models for next-generation WSN applications.