A Control Volume-based numerical study of laminar flow and heat transfer of a single-phase Covalently functionalized Graphene Nanoplatelets (CGNPs)/H2O green nanofluid in a liquid block heat sink with novel fin design and nature-based algorithms (honeycomb, ternate veiny, snowflake, and spider netted) baseplate’s designs conducted for cooling of Central Processing Unit (CPU) in the electronic package. The User Defined Function (UDF) code was used to apply the temperature-dependent thermos-physical properties of CGNPs/H2O green nanofluid to the ANSYS-Fluent 2021 R2. The influence of Reynolds number variation, nanoparticles volume fraction, and the baseplate’s designs on the CPU temperature, pumping power, Heat Transfer Coefficient (HTC), and thermal efficiency of the heat sink have been analyzed. The spider netted baseplate design reduced the maximum temperature of the liquid block by about 8.5K in comparison with the Ternate veiny baseplate design. The heat transfer coefficient has a direct relationship with nanofluid concentration and Reynolds number and the best case in terms of HTC improvement is related to (CGNPs 0.100%wt/H2O,Re=2000) with HTC about 8582.3 W.m-2K-1. Moreover, the most thermal output improvement in comparison with the simple model liquid block heat sink is about 8.5% which is related to the liquid block with spider netted baseplate design and CGNPs 0.075%wt/H2O green nanofluid flow.