  Perovskite Quantum Dots (PQDs) have a promising future for several
applications due to their unique properties. This study investigates the
effectiveness of Machine Learning (ML) in predicting the size, absorbance (1S
abs) and photoluminescence (PL) properties of $\mathrm{CsPbCl}_3$ PQDs using
synthesizing features as the input dataset. the study employed ML models of
Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random
Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT) and Deep
Learning (DL). Although all models performed highly accurate results, SVR and
NND demonstrated the best accurate property prediction by achieving excellent
performance on the test and training datasets, with high $\mathrm{R}^2$ and low
Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values.
Given that ML is becoming more superior, its ability to understand the QDs
field could prove invaluable to shape the future of nanomaterials designing.
