Machine learning has enhanced rice grain classification by enabling accurate, automated, and objective morphological analysis, supporting quality control and varietal selection. This study compared the performance of several algorithms in identifying three Ecuadorian rice varieties (INIAP-11, INIAP-12, and INIAP-20) using a balanced dataset of morphological features. Five models were trained with cross-validation and evaluated using multi-class metrics. Significant differences among varieties particularly in area, length, and eccentricity confirmed their discriminative potential. Initially, models were trained using all morphological variables. However, to optimize training time and computational cost, the study also evaluated model performance after applying dimensionality reduction through Principal Component Analysis (PCA). This approach enabled assessing whether reduced feature spaces could maintain competitive predictive performance while improving efficiency. Overall, all algorithms performed well, but only the Artificial Neural Network (ANN) and Support Vector Classifier (SVC) demonstrated strong generalization without overfitting. In contrast, Random Forest achieved perfect accuracy in training but decreased performance in testing. In conclusion, ANN and SVC emerged as the most robust alternatives for rice grain morphological classification, while the PCA results highlight the value of dimensionality reduction as a strategy to enhance computational scalability without substantially compromising accuracy. The objective of the present study is to train, evaluate, and compare different machine learning algorithms for the classification of three types of rice grains, in order to determine the best model for this task based on seven morphological characteristics of the grains applying machine learning algorithms with and without dimensional reduction.