We comprehensively discuss state-of-the-art integrated guidance and control (IGC) systems, in applications ranging from guided missiles to unmanned vehicles. Unlike separate guidance and control systems, IGC systems consider both control and guidance loops simultaneously, taking into account the cross-coupling relations and the limitations between the two loops. We highlight the pros and cons of the existing IGC algorithms while pointing out several research opportunities and potential research challenges associated with each technique. We envisage that future research direction will be gradually shifted toward the use of artificial intelligence (AI), such as intelligent neuro-fuzzy systems, to achieve adaptive IGC systems that can perform intelligent self-learning and online optimization with minimum human intervention. Finally, this review also delivers an open message to encourage collaborations among experts from multiple disciplines, such as control systems, robotics, and machine learning. The development of multiple AI algorithms to tackle current issues in IGC systems will transform and create novel hybrid knowledge of intelligent IGC systems, paving the way to new applications in multiple robotic platforms.