Sperm quality influences bovine in vitro embryo production (IVEP). Linear regression is a statistical tool that models the relationship between a dependent variable and one or more independent variables. It can be used to predict outcomes, analyze trends, and understand the impact of variables. These models are useful for indicating which sperm variables most influence IVEP results, facilitating the selection of superior samples to enhance IVEP. Using early IVEP indicators, such as cleavage rate, can assist in scheduling recipient preparation. This work aimed to construct linear regression models to study the influence of a comprehensive set of sperm variables and cleavage rate on IVEP yields. A dataset comprising 51 semen batches from 23 Nellore bulls was compiled, including 26 sperm variables from computer-assisted sperm analysis (CASA) and flow cytometry per batch, with 184 IVEP procedures. The most robust predictive model had a coefficient of determination of 0.6358; furthermore, the BULL variable was the most influential predictor, yielding an independent coefficient of determination of 0.5218. Models that were exclusively founded on sperm analysis yielded meager coefficients of determination (<0.04). However, to predict the best batch from a bull, individual models achieve coefficients of determination ranging from 0.58 to 0.99. Contributions, impacts, and positive or negative correlations of various sperm variables with in vitro performance were influenced by the bull. We conclude that the BULL variable was the dominant predictor of in vitro performance, with cleavage rates serving as an early estimator of blastocyst rates. The predictive utility of analyzed sperm traits remains limited. Nonetheless, individualized models offer a valuable tool for selecting optimal batches for preferred bulls within IVEP laboratories, culminating in heightened blastocyst rates.