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Abstract The privatisation of unmanned aerial vehicles (UAVS) used for situational awareness purposes to ensure their own situational awareness based on parameters gives direction to progress and provides a basic framework for future studies. In this context, a unique communication system architecture was proposed for obtaining state-of-the-art mini-UAV data and evaluations were carried out on the basis of data flow and parameters. Within the scope of the evaluation this study postulates a trailblazing approach as a means of optimising flight data pattern recognition by integrating Cross Industry Standard Process for Data Mining (CRISP-DM) and long short-term memory (LSTM)-based predictive depiction. By leveraging the structured framework of CRISP-DM and the sequential learning capabilities of LSTM, this research aims to enhance the accuracy of mini unmanned aerial vehicle systems (UAVS) flight scenario predictive reliability. This framework is applied to navigation phase, where the overall flight trajectory can be seen, and accurate forecasting and pattern recognition are critical for optimising operational efficiency. The findings have displayed the ability to perform high-accuracy predictions of flight parameters within a structured process. The experimental results demonstrate that key flight parameters can be predicted with near–comma-level numerical accuracy, indicating a high level of estimation precision. Through facilitating immediate data transfer and organised navigation phase evaluation, this research provides a methodical strategy for managing flight data, simultaneously contributing notably to UAV decision support systems and self-qualification engineering.