The integration of AI in military operations has enhanced the ability to detect camouflaged objects, as evidenced by the increased use of drones in conflicts such as those in the Middle East and the Ukraine-Russia war. This shift towards AI-powered detection systems necessitates a reevaluation of camouflage evaluation methods and metrics. This systematic literature review examines the evolution of camouflage techniques from naturalistic patterns to advanced designs taking into account AI and drone technology. It explores both traditional and modern evaluation methods, focusing on their applicability in military contexts. The study employs a mixed-methods approach, combining qualitative and quantitative analysis to provide a comprehensive evaluation of camouflage effectiveness. It also incorporates context-specific and generalisable metrics to ensure thorough evaluation across different environments and scenarios. The findings highlight the importance of developing robust evaluation techniques that can address the challenges posed by both human and AI detection systems. This review underscores the need for continuous improvement in camouflage design and evaluation to maintain the effectiveness of military strategies in modern warfare.