The article discusses the development of the Mitigation-Inventory Tool for Integrated Climate Action (MITICA), a framework designed to enhance transparency in climate efforts by integrating greenhouse gas (GHG) inventories, mitigation policies and measures (PAMs), and GHG projections. MITICA employs a hybrid decomposition approach combining machine learning regression techniques with classical forecasting methods to generate GHG emission projections up to 2050, incorporating over 60 PAMs across Intergovernmental Panel on Climate Change (IPCC) sectors. The tool ensures consistency between reporting elements, aligning with IPCC best practices and linking climate change with sustainable economic development. MITICA's results are validated through cross-validation against test data and employ robust methods for evaluating PAMs, establishing its reliability. The paper outlines the framework of MITICA, its forecasting approach, and the accountability for PAMs. MITICA's methodology for projecting GHG emissions in the WOM scenario involves a hybrid model named Artificial iNtelligeNce And cLassIcal STatistics (ANNALIST), which integrates LASSO, SARIMAX, and Random Forest Regression. This model decomposes the trend and noise, applies the Exponentially Weighted Moving-Average (EWMA) algorithm to derive the trend, and uses regression techniques on a pool of potential variables for model development. The model undergoes various machine learning analyses to ensure the maximum likelihood of the outcome. MITICA also includes alternative approaches for projecting the WOM scenario, such as a Gradient Boosting Regression (GBR) model and the SARIMAX method. The tool is deployed as a desktop application using Python, ensuring compatibility with various operating systems and providing users with the ability to run MITICA locally, ensuring data privacy and security. The paper also discusses the mitigation impact of PAMs and the definition of WM and WAM scenarios. MITICA provides an extensive list of PAMs, allowing users to define the magnitude of the desired PAM and adjust methodological parameters. The tool aggregates the individual impact assessment of PAMs to produce the WM and WAM scenarios, enabling the definition of scenarios based on national circumstances and stakeholder agreements. The results of MITICA's projections are validated against historical datasets, showing that ANNALIST and SARIMAX yield results closest to the actual data. The tool has been tested with various input datasets, including the Tajikistan national GHG inventory, confidential information from Uruguay, and simulated databases from the IPCC software. The testing primarily focused on assessing the functionality of the software and identifying any potential bugs. The paper concludes that MITICA addresses the challenges in generating mitigation scenarios and ensuring consistent reporting under the Enhanced Transparency Framework (ETF). It establishes an integrated methodological framework for mitigation scenario production, ensuring consistency between national GHG emission inventories, PAMs, and projections. The tool allows for the transparent generation of mitigation scenarios, facilitating NDC design and tracking, as well asThe article discusses the development of the Mitigation-Inventory Tool for Integrated Climate Action (MITICA), a framework designed to enhance transparency in climate efforts by integrating greenhouse gas (GHG) inventories, mitigation policies and measures (PAMs), and GHG projections. MITICA employs a hybrid decomposition approach combining machine learning regression techniques with classical forecasting methods to generate GHG emission projections up to 2050, incorporating over 60 PAMs across Intergovernmental Panel on Climate Change (IPCC) sectors. The tool ensures consistency between reporting elements, aligning with IPCC best practices and linking climate change with sustainable economic development. MITICA's results are validated through cross-validation against test data and employ robust methods for evaluating PAMs, establishing its reliability. The paper outlines the framework of MITICA, its forecasting approach, and the accountability for PAMs. MITICA's methodology for projecting GHG emissions in the WOM scenario involves a hybrid model named Artificial iNtelligeNce And cLassIcal STatistics (ANNALIST), which integrates LASSO, SARIMAX, and Random Forest Regression. This model decomposes the trend and noise, applies the Exponentially Weighted Moving-Average (EWMA) algorithm to derive the trend, and uses regression techniques on a pool of potential variables for model development. The model undergoes various machine learning analyses to ensure the maximum likelihood of the outcome. MITICA also includes alternative approaches for projecting the WOM scenario, such as a Gradient Boosting Regression (GBR) model and the SARIMAX method. The tool is deployed as a desktop application using Python, ensuring compatibility with various operating systems and providing users with the ability to run MITICA locally, ensuring data privacy and security. The paper also discusses the mitigation impact of PAMs and the definition of WM and WAM scenarios. MITICA provides an extensive list of PAMs, allowing users to define the magnitude of the desired PAM and adjust methodological parameters. The tool aggregates the individual impact assessment of PAMs to produce the WM and WAM scenarios, enabling the definition of scenarios based on national circumstances and stakeholder agreements. The results of MITICA's projections are validated against historical datasets, showing that ANNALIST and SARIMAX yield results closest to the actual data. The tool has been tested with various input datasets, including the Tajikistan national GHG inventory, confidential information from Uruguay, and simulated databases from the IPCC software. The testing primarily focused on assessing the functionality of the software and identifying any potential bugs. The paper concludes that MITICA addresses the challenges in generating mitigation scenarios and ensuring consistent reporting under the Enhanced Transparency Framework (ETF). It establishes an integrated methodological framework for mitigation scenario production, ensuring consistency between national GHG emission inventories, PAMs, and projections. The tool allows for the transparent generation of mitigation scenarios, facilitating NDC design and tracking, as well as