Machine learning (ML) methodologies have proven to be highly advantageous due to their capacity to replace time-intensive QC calculations. Several recent studies have harnessed ML techniques to investigate molecular clusters. To assist in the creation of ML models for molecular clusters studies, we introduce Jammy Key for Machine Learning (JKML). JKML streamlines the training of ML models and their various applications such as predictions of energy/forces and even the creation of ML-based calculators. These calculators can effectively replace QC programs, enabling swift geometry optimization and molecular dynamics simulations, closely replicating the potential energy surface that was used to train the ML calculator. To train ML models, a considerable database of training data is necessary. We have, therefore, upgraded the Atmospheric Cluster Database (ACDB), originally introduced by Elm., to ACDB 2.0. In the new ACDB 2.0, cluster properties are stored in a single compressed file, which is easily manipulated by JKQC to use within JKML. The combination of JKCS, JKQC, JKML, and ACDB 2.0 provides the necessary tools to efficiently model a large variety of molecular clusters.