This paper presents an investigation into the de- velopment of generic Quick Access Recorder (QAR) fuel flow regression models applied to Automatic Dependent Surveillance- Broadcast (ADS-B) data, with the aim of improving the accuracy of fuel flow estimates for various aircraft operations. Given the critical need for accurate fuel consumption estimates to mitigate the environmental impact of aviation, this study explores a novel approach that integrates derivative features and aircraft-specific parameters into a unified model. This approach not only aims to generalise across different aircraft types, thereby providing scalability and flexibility for end users, but also demonstrates adaptability to missing parameters through data augmentation techniques. Using a dataset of QAR data from various aircraft type, this paper evaluates the performance of the model across different flight phases and aircraft types and compares it with other common fuel flow models.