In the era of data-driven modeling, ensuring security and maintaining privacy remains critical. Physical and logical isolation offered by air-gap technology requires added security due to a wide range of high-profile attacks that threaten to compromise air-gapped networks. Sophisticated attack vectors and malware have caused significant damage by accessing sensitive information. The capability of Machine Learning techniques surpasses that of static systems and can offer added protection. ML models can reduce the likelihood of malicious attacks by identifying anomalies, using historical data, and recognizing patterns. An exponential growth of big data shaping critical decisions across industries can be observed. Balancing the needs of Big Data with air-gapped systems’ protective benefits to explore specific technologies used in hybrid air-gapped models for Big Data. This paper examines the ML applications in air-gap networks, including the cloud, by understanding the use cases and practical challenges in implementation. The results of this review and subsequent analysis intend to offer insights into scaling the benefits of air-gapped networks by exploring hybrid models.