Field-programmable gate arrays (FPGAS) have emerged as a powerful platform for real-time image processing due to their inherent parallelism and configurability. This paper presents an optimized hardware implementation of fundamental image processing algorithms including Sobel edge detection, Thresholding contrast stretching and negative transformation using VHDL on FPGA architecture. Unlike conventional CPU and GPU-based systems, which rely on sequential execution, the proposed design exploits spatial and temporal parallelism to achieve high throughput and low latency. pipelined architecture combined with line buffering and window-based processing is adapted to efficiently process streaming pixel data. The system is validated using modalism simulation and Mat lab-based verification. Experimental results demonstrate that the FPGA implementation processes a 256×256 image frame in 0.019 seconds at a 10 MHZ clock frequency achieving approximately 10×–13× speed improvement over Mat lab-based software execution. The proposed architecture also ensures efficient resource utilization and is scalable for real-time applications such as surveillance, object detection and embedded vision systems. the results confirm that FPGA-based implementations provide a viable solution for high-performance, low-power image processing and industrial Robots, 5G Networks And AI Inference Applications.