The paper provides an in-depth exploration of the integration of Internet of Things (IoT) technologies with cloud, fog, and edge computing paradigms, examining their transformative impact on computational architectures. It begins by overviewing the evolution and global adoption of IoT, emphasizing the increasing importance of integrating cloud, fog, and edge computing to meet the escalating demands for real-time data processing, low-latency communication, and scalable infrastructure in the IoT ecosystem. The survey meticulously dissects each computing paradigm, highlighting their unique characteristics, advantages, and challenges. The paper discusses the individual strengths and limitations of IoT, cloud computing, edge computing, and fog computing, addressing issues such as latency, bandwidth consumption, security, and data privacy. It explores the synergies between IoT and cloud computing, recognizing cloud computing as a backend solution for processing vast data streams generated by IoT devices. The integration of edge computing with IoT is investigated, showcasing the symbiotic relationship where edge nodes leverage the residual computing capabilities of IoT devices to provide additional services. The challenges associated with the heterogeneity of edge computing systems are highlighted, and the paper presents research on computational offloading as a strategy to minimize latency in mobile edge computing. The paper also acknowledges challenges related to unreliable data handling and privacy concerns, emphasizing the need for robust security measures and regulatory frameworks. It concludes with insights into the collaborative integration of cloud, fog, and edge computing to form a cohesive computational architecture for IoT. The future perspectives section anticipates the role of 6G technology in unlocking the full potential of IoT, emphasizing applications such as telemedicine, smart cities, and enhanced distance learning. Key areas for future research include cybersecurity, energy consumption, and standardization challenges.The paper provides an in-depth exploration of the integration of Internet of Things (IoT) technologies with cloud, fog, and edge computing paradigms, examining their transformative impact on computational architectures. It begins by overviewing the evolution and global adoption of IoT, emphasizing the increasing importance of integrating cloud, fog, and edge computing to meet the escalating demands for real-time data processing, low-latency communication, and scalable infrastructure in the IoT ecosystem. The survey meticulously dissects each computing paradigm, highlighting their unique characteristics, advantages, and challenges. The paper discusses the individual strengths and limitations of IoT, cloud computing, edge computing, and fog computing, addressing issues such as latency, bandwidth consumption, security, and data privacy. It explores the synergies between IoT and cloud computing, recognizing cloud computing as a backend solution for processing vast data streams generated by IoT devices. The integration of edge computing with IoT is investigated, showcasing the symbiotic relationship where edge nodes leverage the residual computing capabilities of IoT devices to provide additional services. The challenges associated with the heterogeneity of edge computing systems are highlighted, and the paper presents research on computational offloading as a strategy to minimize latency in mobile edge computing. The paper also acknowledges challenges related to unreliable data handling and privacy concerns, emphasizing the need for robust security measures and regulatory frameworks. It concludes with insights into the collaborative integration of cloud, fog, and edge computing to form a cohesive computational architecture for IoT. The future perspectives section anticipates the role of 6G technology in unlocking the full potential of IoT, emphasizing applications such as telemedicine, smart cities, and enhanced distance learning. Key areas for future research include cybersecurity, energy consumption, and standardization challenges.