Machine learning (ML) holds significant potential in nanotechnology, enabling predictions of nanomaterial structure and properties, facilitating materials design, and reducing the need for time-consuming experiments. While ML applications for achiral nanomaterials are well-established, its use for chiral nanomaterials remains limited. This review discusses ML methods for studying both achiral and chiral nanomaterials, emphasizing their application in understanding synthesis-structure-property relationships and in developing new sustainable chiral materials with high optical activity and enantioselectivity. It also highlights the use of ML in analyzing structural chirality via electron microscopy. The review outlines key ML approaches, including supervised, unsupervised, and semi-supervised learning, as well as deep learning, and their applications in nanomaterials research. It discusses challenges in data quality and the importance of data-centric approaches, along with methods for data labeling and augmentation. The review also covers ML-enhanced data analysis for identifying synthesis-structure-property correlations and predicting NP properties. It emphasizes the potential of ML in accelerating materials discovery and design, particularly for chiral nanomaterials, and highlights the need for high-quality, diverse datasets to improve ML performance. The review concludes with a discussion of future directions in ML applications for chiral nanomaterials, including the use of transfer learning and large language models for predicting material properties with limited data.Machine learning (ML) holds significant potential in nanotechnology, enabling predictions of nanomaterial structure and properties, facilitating materials design, and reducing the need for time-consuming experiments. While ML applications for achiral nanomaterials are well-established, its use for chiral nanomaterials remains limited. This review discusses ML methods for studying both achiral and chiral nanomaterials, emphasizing their application in understanding synthesis-structure-property relationships and in developing new sustainable chiral materials with high optical activity and enantioselectivity. It also highlights the use of ML in analyzing structural chirality via electron microscopy. The review outlines key ML approaches, including supervised, unsupervised, and semi-supervised learning, as well as deep learning, and their applications in nanomaterials research. It discusses challenges in data quality and the importance of data-centric approaches, along with methods for data labeling and augmentation. The review also covers ML-enhanced data analysis for identifying synthesis-structure-property correlations and predicting NP properties. It emphasizes the potential of ML in accelerating materials discovery and design, particularly for chiral nanomaterials, and highlights the need for high-quality, diverse datasets to improve ML performance. The review concludes with a discussion of future directions in ML applications for chiral nanomaterials, including the use of transfer learning and large language models for predicting material properties with limited data.