Approximate computing enhances energy efficiency by allowing hardware to deliver sufficiently accurate results tailored to specific applications, prioritizing energy savings over absolute precision. An essential domain of interest lies in advancing adder units, crucial for applications ranging from machine learning to signal, image, and video processing. These units are also fundamental in more complex operations like subtraction, comparison, multiplication, squaring, and division. This paper focuses on synthesizing approximate parallel prefix adders. Unlike current methods that focus on specific architectures, the synthesizer generates all solutions meeting the designer's requirements, offering a range of delay, area, and error trade-offs. This automated exploration of design space leads to near-optimal solutions unattainable with traditional architectures. The synthesized adders achieve a 40–55% reduction in area and improve image quality metrics by up to 21.49% in PSNR and 24.14% in MSSIM for an image filter application compared to an exact parallel-prefix adders adder.