The efficient allocation of resources in high-performance computing (HPC) depends on accurate analysis of application behavior. However, these scalability analyses are often computationally expensive and time-consuming. Although there are tools that automate this process, such as PaScal Suite, the cost of execution is still a challenge. To mitigate these costs, prediction strategies have proven effective, notably the use of neural networks as regression models. The obstacle, however, lies in choosing the data to train these models: the process can be slow and expensive, as depending on the order of the data, it may require that nearly all of the application’s settings be configured in PaScal Suite. This work proposes a strategy based on Reinforcement Learning (RL) to optimize the choice of samples in performance modeling. The main goal is to minimize the number of executions required to train a regression model, while ensuring high accuracy. The methodology employs a Deep Q-Learning agent that iteratively selects the ideal configurations (number of cores and problem size). These selected data are then used to train a neural network capable of predicting the execution time for each configuration, always aiming to maximize information gain and minimize time spent. Experimental analyses were performed to evaluate this approach in comparison to random and heuristic methods. Results demonstrate that the strategy reduced the total exhaustive execution time by approximately 71% and outperformed the random search baseline by nearly 40%, all while maintaining an error rate of less than 10%. This validates the feasibility of using RL to drastically reduce time costs in scalability prediction without compromising accuracy.