A fast calculated kernel matrix method is coupled with the compressed matrix technique (FKMCT) to solve the large-scale gravity forward modelling problem. This method accelerates the coefficient matrix computation by reducing the arctangent, logarithm and multiplication functions in the prismatic gravity analytical expression. In addition, the use of the compressed matrix technique presents a significant advantage, which does not require the storage of redundant kernel matrices, further reducing the memory requirements and computation time. Moreover, the discrete convolution of the compressed matrix with density is executed through the 2D Fast Fourier Transform (FFT). Two typical synthetic models are used to test the performance of the novel algorithm. The results demonstrate that the developed algorithm is approximately 15 times faster than the traditional algorithm. Concurrently, it demands nearly 1/7th of the memory while ensuring equivalent computational accuracy. To further illustrate the capability of the algorithm, we applied our method to terrain correction of an airborne gravity dataset using a real digital elevation model. Our approach efficiently calculated 250,000 observation points across 25 million cells in just 5.42 seconds, compared to approximately one day for traditional methods based on the traditional analytic expression for a prism.