Several applications are becoming widespread where autonomous drones are used in delivery, surveillance, and disaster management, with the demand for quick and precise real-time responses to manoeuvre through complex and dynamic environments with minimal risk or casualties. Nevertheless, existing onboard processing systems are unable to provide the required low-latency AI inference due to the restricted computational power, high energy usage, and weight factors that negatively impact the flight time. These factors also adversely affect the weight defence of the plane. To overcome these issues, this paper presents a new high-speed, low-energy consumption System-on-Chip (SoC) designed with the novel purpose of onboard AI inference for autonomous drone navigation systems. The suggested SoC is a heterogeneous multi-core design that incorporates lightweight RISC-V cores, along with specialised utilities built on the available quantised neural network style, designed to process Wi-Fi and TL network models combined with real-time sensor fusion, which provides this with multi-prepared inputs, LIDAR, IMUs, and GPS, which can personally monitor and reply with a minimal delay. It also features an intelligent power control unit, which utilises dynamic voltage and frequency scaling to achieve optimal power usage at various stages of flight. It features a low-latency interconnect fabric that enables the rapid exchange of data between AI accelerators and sensor modules, as well as built-in security features to ensure that web-based attacks on drone control systems are successfully intercepted. Simulation and prototype testing have demonstrated that the proposed SoC can infer Latencies of less than 5 ms with power consumption under 2 W, achieving 30/25 performance and power improvements over existing embedded AI systems with similar accuracy and precision. This enables critical functions such as navigation, including obstacle avoidance and path planning. This work corroborates that a specialised SoC design methodology is effective in facilitating real-time, predictable, and power-efficient AI-driven autonomous drone navigation, with foundations in a safer and stronger aerial environment.