Artificial intelligence has the potential to address many societal, economic, and environmental challenges, but only if AI-enabled gadgets are kept secure. Many artificial intelligence (AI) models produced in recent years can be hacked by utilizing cutting-edge techniques. This issue has sparked intense research into adversarial AI to develop machine and deep learning models that can withstand various types of attacks. We provide a detailed summary of artificial intelligence in this paper to prove how adversarial attacks against AI applications can be mounted, covering topics such as confrontational knowledge and capabilities, existing methods for actually producing adversarial examples, and existing cyber defense models. In addition, we investigated numerous cyber countermeasures that could defend AI applications against these attacks and offered a systematic approach for demonstrating war strategies against machine learning and artificial intelligence. To safeguard AI applications, w