This paper presents an 8x8 Hybrid Approximate Multiplier using Approximate Parallel Prefix Adders (AxPPAs) to maximize performance for error-tolerant applications such as machine learning and image processing. With no increase in power consumption, the suggested design achieves notable advantages by selectively applying approximations to the least significant bits of hybrid adders, which combine Kogge Stone and Brent Kung architectures. This results in a reduction of slice LUTs of 24.675%, a reduction of occupied slices of 20.68%, and a reduction of delay of 9.33%. The results of error analysis show an MRE of 2.999% and an MAE of 85. These outcomes show how the suggested design can balance efficiency and accuracy in high-speed, resource-constrained VLSI systems. The study also emphasizes how the design can be modified to fit different FPGA architectures, making it a flexible answer to modern computing requirements.