Rubberized concrete is one of the solutions to the disposal problem caused by large amounts of untreated waste rubber. To assess the performance of existing concrete structures, non-destructive-testing techniques offer a direct, quick, safe and reliable means of assessing the performance of concrete structures. Several researchers have proposed relationships, often of an exponential form, between the Ultrasonic Pulse Velocity (UPV) and compressive strength of rubberized concrete. This paper aims to propose a regression model and a genetic algorithm based backpropagation neural network (GA-BPNN) model that can be used to estimate compressive strength of rubberized concrete with UPV for different applications and accuracy requirements. Regression model in comparisons with GA-BPNN, firstly requires less computation work and will be easier for site measurement or environments without computers or certain softwares, secondly it has no barriers for users who are not familiar with machine learning models to approximately estimate the strength. The regression model comprises an Adjusted Regression Model which is a multi-variable non-linear model adjusted based on the ordinary exponential model incorporating other principal parameters, hence representing an improvement on the existing exponential model and two types of Stepwise Regression Model (pure linear and pure quadratic) will be employed. To achieve this, a database containing 158 pairs of data collected from previous literature is compiled. Results indicate that both three types of regression models and GA-BPNN are capable of effectively predicting the compressive strength of rubberized concrete with reasonable values of statistical indexes. More specifically, among three types of regression model, the pure quadratic stepwise regression model has relatively better performance with higher R and lower root-mean-square error values. Results also support that GA-BPNN has the highest accuracy compared to regression models and is proven to be reasonable for more precise estimations.