The exponential expansion of the Internet of Things (IoT) has altered connection and automation in many industries. The vast amount of data produced and the restricted processing power of various IoT devices have caused major security issues, however, as the Internet of Things (IoT) has grown quickly. This paper offers a thorough examination and analysis of current studies combining deep learning (DL) and big data analytics to improve IoT security. Among the many notable contributions examined were blockchain-integrated security systems, hybrid DL models combining CNNs and RNNs with big data analytics, and anomaly detection in industrial IoT using autoencoders and LSTM. The evaluation also takes into account federated learning strategies meant to provide privacy-preserving security in highly dispersed IoT networks. Though the accuracy—often over 90% in threat detection—is remarkable, other research points out drawbacks include the focused attention on certain frameworks, lack of generalizability across IoT sectors, and difficulties in using hybrid or federated models. This paper underlines the changing function of integrating deep learning with big data analytics by means of insights from 15 major research and discusses the future promise of these technologies in creating safe, scalable, and smart IoT systems. Authors have utilized a hybrid deep learning approach combining Convolutional Neural Networks (CNNs) and autoencoders for anomaly detection within general IoT environments, achieving a high detection accuracy of 92.3%. Some of the author focused specifically on Industrial IoT (IIoT), employing a combination of Long Short-Term Memory (LSTM) networks and autoencoders. This approach yielded an even higher detection accuracy of 94.1%. Meanwhile, many researchers proposed a privacy-preserving model using federated learning, achieving an estimated detection accuracy of around 90%.