Designing drilling fluids, spacers and cement slurries are all often done by trial and error in the laboratory. There are several test data that would not be used in this method and it is hard to digest a plethora of information for users and take intelligent and cost-effective decision to design a fluid with the desired properties. Therefore, trial and error method is considered to be time consuming, very costly and misleading. Today, there is a need for an intelligent system which uses all the available fluid design data stored in a database by which we can benefit from its insights for smart fluid designs. This predictive tool suggest a composition for drilling fluids, spacer fluid or cement slurry by implementing a machine learning algorithm on imported experimental data. This paper investigates and implements a data-driven predictive tool which uses Gaussian Process Regression (GPR). GPR as a novel machine learning method considerably reduces the costs of testing, optimizes the material use, integrates available experimental data and eliminates the user bias. This practical nonlinear regression method fosters an efficient and fast prediction analysis which do not require including complex physics of the underlying intricate chemical fluid behavior while integrating all available data from different databases. GPR has exceptional advantages over traditional regression methods since it does not require a known form for regression function. Also its capability of determining estimation error and confidence interval is unique. This machine learning based tool offers comprehensive insights for intelligent fluid design and considerably reduces the experiment cost. This study showcases an example through which of GPR predicts rheological properties and helped engineers to maintain the fluid rheological hierarchy for better cement jobs and well integrity. Zonal isolation has the utmost importance for carbon sequestration projects.