In Biomedical application, the need for performance enhancement of reliable processors is high. The use of machine learning methods increases the computational complexity and memory requirements. In nanometer SRAM design, the temperature, voltage, and power fluctuation affects the performance in lower CMOS devices. In existing methods higher technology like 180 nm CMOS were used which consumes more power and delay. In addition CMOS below 45 nm is disadvantage due to second order effects. The comparison of digital implementation through FinFETs and planar bulk MOSFETs reveals that the FinFET is advantages over bulk MOSFETs in excellent subthreshold slope, reduced leakage, and better voltage gain. High rejection ratio can be obtained if implemented for filtering of biomedical signals. This paper presents the design of the SRAM for biomedical system with low leakage current and power consumption. The proposed SRAM was implemented in 32 nm CMOS and FinFET technology. For investigation of performance various technologies were used. The predictive technology models were used for simulation Synopsis HSpice.