Today，the aviation industry faces a critical shortage of qualified pilots and escalating labor costs due to the continuous surge in global air transport demand. Consequently，single-pilot operation （SPO） has emerged as a strategic evolution in civil aviation to optimize crew resources and mitigate pilot fatigue，especially during long-haul flights. However，the transition from traditional multicrew cockpits to a single-pilot environment is not merely a reduction in personnel； rather，it introduces a significant “cognitive and decision-making gap” previously filled by the co-pilot. To bridge this gap，this paper proposes the concept of the “AI co-pilot” —— an intelligent system designed to establish a deep cognitive collaborative relationship with the human pilot through advanced perception，semantic understanding，and autonomous decision-making capabilities. This paper provides a systematic and comprehensive review of the core technological components enabling AI-assisted piloting. In this work，we propose a four-layer analytical framework known as “perception-cognition-interaction-decision-safety monitoring”. This structure serves as a blueprint for integrating heterogeneous AI modules into the high-integrity environment of an aircraft cockpit. First，we look into multimodal physiological and behavioral sensing technologies that are essential for the real-time monitoring of a pilot’s workload and fatigue state. Given that traditional subjective assessments are insufficient for high-dynamic flight environments，this review focuses on the integration of electroencephalogram （EEG） and functional near-infrared spectroscopy （fNIRS）. In particular，we provide a critical analysis of their trade-offs： while EEG offers superior temporal resolution capable of capturing instantaneous cognitive shifts，fNIRS demonstrates stronger robustness against motion artifacts in a vibrating cockpit. Furthermore，computer vision （CV） techniques are discussed for the nonintrusive monitoring of facial expressions，percentage of eyelid closure （PERCLOS），and visual tunneling effects. The fusion of these modalities allows the AI Co-pilot to “read” the pilot’s state and trigger adaptive automation upon detection of cognitive overload. Second，the review explores intelligent speech interaction and semantic understanding within the complex controller-pilot data link communications （CPDLC） and radio environments. The aviation context poses unique challenges，including low signal-to-noise ratios in VHF communications and nonstandard accents. In this paper，we analyze the application of LLMs and transformer-based architectures in intent recognition and slot filling for air traffic control （ATC） instructions. By parsing complex，nested instructions into executable parameters，the AI Co-pilot functions as an “intelligent radio operator” that significantly reduces the auditory and manual workload of the single pilot. Third，we summarize intelligent route planning and dynamic conflict resolution driven by the synergy of large models and deep reinforcement learning （DRL）. Algorithms such as proximal policy optimization （PPO） and soft actor-critic （SAC） are evaluated for their efficacy in continuous action spaces and high-dimensional flight dynamics. Unlike traditional rule-based flight management systems （FMS），AI-driven planners can explore optimal trajectories in unconstrained airspace and respond adaptively to extreme weather or system failures. Additionally，we emphasize the transition from individual agent optimization to multiagent reinforcement learning （MARL） for collaborative collision avoidance in dense urban air mobility （UAM） scenarios. A significant portion of this review is dedicated to the core barriers that hinder the commercial deployment of aviation AI. We provide an in-depth analysis of the non-interpretability of “black-box” deep learning models，which prevents human pilots from establishing calibrated trust during time-critical emergencies. Furthermore，the paradigm conflict between the nondeterministic nature of AI and traditional deterministic airworthiness standards （e.g.，DO-178C） is dissected. We also discuss the latest policy trends from EASA and the FAA，highlighting the shift toward “run-time assurance （RTA）” frameworks. Specifically，RTA acts as a safety shield，bounding the outputs of complex AI models within a predefined safe flight envelope and ultimately providing a viable certification pathway. Finally，this paper presents a perspective on future trends. We posit that the next generation of aviation AI will evolve toward a “multimodal unified architecture” in which text，speech，vision，and flight dynamics are aligned in a single latent space. We predict that this evolution will transform the AI Co-pilot from a collection of isolated subtasks into a holistic “cognitive partner”. By emphasizing transparency，bidirectional communication，and standardized certification，this review aims to provide a theoretical foundation and engineering reference for achieving high-transparency，deep human-machine collaborative intelligent cockpits in the SPO era.