In recent decades the demands for area efficient discrete wavelet transform (DWT) has been emerging steadily within the field of digital image & vision analysis. Among other transform model wavelets allows to decomposing the inputs into various bands in accordance with the frequency ranges. In general discrete Wavelet Transform (DWT) requires large number of multiplication units for multi level transformation. But HAAR wavelet requires only accumulation units since it comprise of wavelet coefficients with smaller integers (+1 or -1). In this paper optimized HAAR wavelet is introduced with Kogge–Stone based parallel-prefix topology for accumulation. This prefixbased model offers high-speed solution for HAAR DWT in image processing applications. Here in this work Modified Carry Correction block is introduced to optimize the KSA PPA and optimal clock Dividers/Reset Counter is used for data flow control. To maximize thesystem performance with improved reliability and the applicability of proposed design here configurable critical path delay optimization is introduced which can allows DWT to operate at variable rate. The flexible hardware architecture of multilevel decomposition is presented in this research. Discrete.For image compression applications, the Wavelet Transform (DWT) is suggested to remove extraneous data from the sent images or video frames over the wireless channel. The Very High Speed Integrated Circuit (VHSIC) Hardware Description Language (VHDL)-based methodology is used to describe and synthesise the DWT architecture. With a few minor adjustments, the design can be implemented on any target Field Programmable Gate Array (FPGA) device. It supports images with resolutions of 64 x 64, 128 x 128, 256 x 256, and 512 x 512 pixels and has a seven-level deconstruction capability. n. The Fast Haar Wavelet Transform (FHWT) is used to reduce computational complexity. The 2D DWT multilevel FPGA core's decreased resource utilisation can be used to work around the severe hardware limitations of a variety of wireless and mobile device applications.