This study investigates the performance of multiple classification techniques applied to a nanoparticle dataset described by physicochemical properties. Classical machine learning models including Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (K-NN), and Decision Tree were evaluated alongside a Multi-Layer Perceptron (MLP) neural network. While SVM achieved notable performance among classical models with an accuracy of 87.38%, the MLP significantly outperformed all other methods, reaching 94.29% accuracy, the lowest RMSE, and the highest R2. Moreover, the MLP demonstrated superior robustness in managing class imbalance, reflected in its high F1-score. These findings highlight the advantage of deep learning models in capturing complex nonlinear patterns inherent to nanoparticle data, providing a novel perspective for improving the accuracy and reliability of nanoparticle classification.