This review article explores the application of machine learning (ML) in the field of nanomaterials, particularly focusing on chiral nanomaterials. ML holds significant potential in nanotechnology by enabling predictions of nanomaterial structures and properties, facilitating material design, and reducing the need for time-consuming experiments and simulations. However, the application of ML in chiral nanomaterials is still in its early stages, despite the potential for developing sustainable chiral materials with high optical activity, circularly polarized luminescence, and enantioselectivity. The article provides an overview of ML methods used for studying achiral nanomaterials and offers guidance on adapting these methods to chiral nanomaterials. It discusses the synthesis-structure-property relationships, forward and inverse design, and the use of ML in various applications such as catalysis, electronics, and medicine. The review also highlights key recent publications and the challenges and future prospects in the field. Key points include: 1. **Introduction to ML**: ML has transformed various fields, including nanotechnology, by enabling data analysis, pattern recognition, and predictive modeling. 2. **ML Approaches in Nanotechnology**: ML methods are used to analyze synthesis-related and property-related data, with a focus on data quality, representation, and exploration. 3. **Data Quality and Representation**: High-quality data is crucial for accurate ML predictions. The article discusses data labeling, augmentation, and the importance of open-source datasets. 4. **Data Exploration with Supervised Learning**: ML techniques are used to explore data, identify correlations, and predict properties based on synthesis parameters. 5. **Synthesis-Structure-Property Correlations**: ML algorithms can predict and optimize material properties, reducing the need for experimental validation. 6. **Chiral Nanomaterials**: The review emphasizes the importance of chiral nanomaterials in biology and medicine, and how ML can aid in their study and development. The article concludes by summarizing the achievements, challenges, and future outlook for the application of ML in chiral nanomaterials, highlighting the potential for significant advancements in sustainable and efficient material design and discovery.This review article explores the application of machine learning (ML) in the field of nanomaterials, particularly focusing on chiral nanomaterials. ML holds significant potential in nanotechnology by enabling predictions of nanomaterial structures and properties, facilitating material design, and reducing the need for time-consuming experiments and simulations. However, the application of ML in chiral nanomaterials is still in its early stages, despite the potential for developing sustainable chiral materials with high optical activity, circularly polarized luminescence, and enantioselectivity. The article provides an overview of ML methods used for studying achiral nanomaterials and offers guidance on adapting these methods to chiral nanomaterials. It discusses the synthesis-structure-property relationships, forward and inverse design, and the use of ML in various applications such as catalysis, electronics, and medicine. The review also highlights key recent publications and the challenges and future prospects in the field. Key points include: 1. **Introduction to ML**: ML has transformed various fields, including nanotechnology, by enabling data analysis, pattern recognition, and predictive modeling. 2. **ML Approaches in Nanotechnology**: ML methods are used to analyze synthesis-related and property-related data, with a focus on data quality, representation, and exploration. 3. **Data Quality and Representation**: High-quality data is crucial for accurate ML predictions. The article discusses data labeling, augmentation, and the importance of open-source datasets. 4. **Data Exploration with Supervised Learning**: ML techniques are used to explore data, identify correlations, and predict properties based on synthesis parameters. 5. **Synthesis-Structure-Property Correlations**: ML algorithms can predict and optimize material properties, reducing the need for experimental validation. 6. **Chiral Nanomaterials**: The review emphasizes the importance of chiral nanomaterials in biology and medicine, and how ML can aid in their study and development. The article concludes by summarizing the achievements, challenges, and future outlook for the application of ML in chiral nanomaterials, highlighting the potential for significant advancements in sustainable and efficient material design and discovery.