Border security is a major concern due to terrorist activities, illegal migrants, and smuggling. Illegal migrants, including unauthorized travellers, tourists, and criminals, pose threats to borders. Smuggling involves illegal goods, including drugs and counterfeits. Detecting these threats is crucial for maintaining peace and harmony. This study evaluates the use of Artificial Intelligence (AI) in remote border surveillance, focusing on machine learning, natural language processing, and computer vision. It also discusses the limitations of AI in detecting border intrusion in surveillance systems. The study suggests that AI-based systems can be used for border security and surveillance in remote areas, removing human errors and safeguarding against intrusion, terrorists and smugglers. These systems, integrating machine learning, natural language processing, and computer vision, can detect unauthorized movements, illegal crossings, and terrorist activities. However, concerns like privacy, ethical use and algorithm bias need to be addressed. Regular algorithm improvements are necessary to ensure the effectiveness of these AI-based systems.