Sinkhole attacks represent a significant security threat in Wireless Sensor Networks (WSNs) and the Internet of Things (IoT), where compromised nodes deceitfully attract network traffic to disrupt normal operations. This survey paper comprehensively reviews contemporary techniques developed to detect and mitigate sinkhole attacks, comparing their methodologies, strengths, and limitations. Various approaches are explored, including knowledge-based rules, cross-layer integrations, Machine learning models, trust-based protocols, and algorithmic detection methods. Each technique is assessed based on its detection accuracy, implementation complexity, resource requirements, and adaptability to evolving threats. The comparative analysis aims to provide insights into the effectiveness of these methods under different network conditions and attack scenarios. By highlighting the advantages and disadvantages of each approach, this survey has taken several research papers to analyze the resilience of WSNs and IoT networks against sinkhole attacks. Through this detailed examination, the paper underscores the necessity for integrated and adaptive solutions that balance security, efficiency, and scalability to safeguard the growing landscape of interconnected devices.