Conventional communication systems are designed to transmit data reliably, focusing on metrics such as bit error rate, throughput, and channel capacity. However, these traditional measures do not evaluate whether the received information is actually useful for a specific downstream task. To address this limitation, semantic communication has emerged as a promising paradigm that prioritizes the meaning, context, and task relevance of transmitted information rather than raw bit level reconstruction. This paper presents a comprehensive review of the theoretical foundations, system architectures, learning techniques, and evaluation practices in semantic communications. We classify existing state-of-the-art algorithms ranging from deep joint source-channel coding (Deep JSCC) and task-oriented feature transmission to generative diffusion models and contrast their operational trade offs against conventional baselines. Furthermore, we examine key application domains, including remote sensing, autonomous vehicles, and healthcare IoT, while addressing open challenges in security, privacy, robustness, and standardized evaluation.

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