Abstract Due to recent advances in IoT (Internet of Things) technologies, availability of reliable data and emergence of machine learning, bio-inspired learning and artificial intelligence, has demonstrated its ability to solve the large complex problems which is not possible before. In particular, machine learning and bio-inspired learning algorithms provides the effective solutions in image processing techniques. However, the implementation of the above-mentioned algorithms in the general CPU requires the intensive usage of bandwidth, area and power which makes the CPU unhealthy of usage and implementation. To overcome this problem, ASIC (application specific integrated circuits), GPU (Graphics Processing Unit) &FPGA (Field Programmable gate arrays) have been employed to improve the performance of the hybrid machine learning (ML) classifiers and deep learning algorithms. FPGA has been recently employed for an effective implementation and to achieve the high performance of the learning algorithms. But integrating the complex learning algorithms in FPGA still remains to be real challenge among the researchers. The paper proposes new reconfigurable architectures for bio- inspired classifiers to diagnosis the medical casualties which can be suitable for the tele health care applications. This paper aim is as follows (i) Design and implementation of Parallel Fusion of FSM and Reconfigurable shared Distributed Arithmetic for Bio-Inspired Classifiers (ii) Development of Accelerator Environment to test the performance of proposed architecture (iii) Performance evaluation of proposed architecture in terms of accuracy of detection in compared with MATLAB simulation iv) Implementation of proposed architectures in different ARtix-7 architectures and determination of power, throughput and area . Moreover, the proposed architecture has been tested with the and compared with the other existing architectures.