Ad hoc networks, including Mobile Ad Hoc Networks (MANETs), are widely used for their adaptability and decentralized architecture, but they face significant challenges in scalability and security. Ensuring data protection in these networks is essential, particularly in resource-constrained environments where computational efficiency is critical. Cryptographic methods, including symmetric and asymmetric encryption, offer foundational security but often incur high computational costs. Selective encryption emerges as a promising solution, applying encryption only to critical data portions to balance security with efficiency. A comprehensive review of related work highlights the advancements in selective encryption for diverse applications such as image, video, and medical data, integrating machine learning to optimize efficiency, scalability, and resilience. Key findings emphasize the potential for selective encryption in various compression algorithms, the need for adaptable and parameterized approaches, and the pitfalls of relying solely on randomization techniques. Future research directions include developing flexible selective encryption methods for specific compression standards like JPEG2000, establishing universal design principles, and enhancing algorithm scalability for real-time and IoT applications. By leveraging advancements in cryptography and machine learning, this study lays the groundwork for innovative encryption solutions that address the evolving demands of modern wireless networks.