The article discusses the development of the Mitigation-Inventory Tool for Integrated Climate Action (MITICA), a novel framework designed to enhance the transparency and consistency of climate mitigation efforts under the Paris Agreement. MITICA addresses significant challenges in defining and reporting greenhouse gas (GHG) mitigation targets, including inconsistencies between national GHG inventories, projections, and mitigation scenarios, as well as the disconnect between mitigation policies and scenarios. The tool integrates a hybrid decomposition approach combining machine learning regression with classical forecasting methods to produce accurate GHG emission projections. MITICA enables the generation of mitigation scenarios up to 2050, incorporating over 60 mitigation policies across IPCC sectors, and ensures consistency between reporting elements, aligning with IPCC best practices and sustainable development goals. The tool's results are validated against observed trends and employ robust methods for evaluating policies, establishing its reliability. MITICA is designed to be universally applicable, particularly for developing countries, by providing a standardized methodology that links national GHG inventories with projections and policies. It uses a consistent framework for all IPCC sectors to minimize inconsistencies while being emission source and country-specific. The tool starts with a 'Without Measures' (WOM) scenario, representing projected emissions under current conditions, and then develops mitigation scenarios (With Measures - WM and With Additional Measures - WAM) by considering the impact of implemented policies. MITICA employs a hybrid model called ANNALIST, which integrates LASSO, SARIMAX, and Random Forest Regression to handle small datasets, long-term forecasting, and exogenous drivers. The model decomposes trends and noise, applies the Augmented Dickey Fuller test for stationarity, and uses machine learning techniques for outlier detection and prediction. The tool has been tested with various datasets, including energy use, goods exports, and alternative energy data, demonstrating its effectiveness in capturing observed trends. MITICA's results show that ANNALIST and SARIMAX provide the closest alignment with actual data, highlighting the tool's reliability. The tool also allows users to select alternative models, such as Gradient Boosting Regression (GBR) and SARIMAX, based on data characteristics. MITICA is deployed as a desktop application using Python, ensuring cross-platform compatibility and accessibility. The article highlights the limitations of MITICA, including the assumption that GHG emissions are solely influenced by proxies, which may overlook natural changes in emission profiles without public intervention. Future improvements could address this by incorporating varying levels of change in endogenous parameters. Additionally, the tool has not been empirically tested against alternative frameworks, and further research is needed to compare it with other models. MITICA's development aims to enhance transparency, consistency, and the ability of countries to track and report on their climate efforts, contributing to the global objectives of the Paris Agreement.The article discusses the development of the Mitigation-Inventory Tool for Integrated Climate Action (MITICA), a novel framework designed to enhance the transparency and consistency of climate mitigation efforts under the Paris Agreement. MITICA addresses significant challenges in defining and reporting greenhouse gas (GHG) mitigation targets, including inconsistencies between national GHG inventories, projections, and mitigation scenarios, as well as the disconnect between mitigation policies and scenarios. The tool integrates a hybrid decomposition approach combining machine learning regression with classical forecasting methods to produce accurate GHG emission projections. MITICA enables the generation of mitigation scenarios up to 2050, incorporating over 60 mitigation policies across IPCC sectors, and ensures consistency between reporting elements, aligning with IPCC best practices and sustainable development goals. The tool's results are validated against observed trends and employ robust methods for evaluating policies, establishing its reliability. MITICA is designed to be universally applicable, particularly for developing countries, by providing a standardized methodology that links national GHG inventories with projections and policies. It uses a consistent framework for all IPCC sectors to minimize inconsistencies while being emission source and country-specific. The tool starts with a 'Without Measures' (WOM) scenario, representing projected emissions under current conditions, and then develops mitigation scenarios (With Measures - WM and With Additional Measures - WAM) by considering the impact of implemented policies. MITICA employs a hybrid model called ANNALIST, which integrates LASSO, SARIMAX, and Random Forest Regression to handle small datasets, long-term forecasting, and exogenous drivers. The model decomposes trends and noise, applies the Augmented Dickey Fuller test for stationarity, and uses machine learning techniques for outlier detection and prediction. The tool has been tested with various datasets, including energy use, goods exports, and alternative energy data, demonstrating its effectiveness in capturing observed trends. MITICA's results show that ANNALIST and SARIMAX provide the closest alignment with actual data, highlighting the tool's reliability. The tool also allows users to select alternative models, such as Gradient Boosting Regression (GBR) and SARIMAX, based on data characteristics. MITICA is deployed as a desktop application using Python, ensuring cross-platform compatibility and accessibility. The article highlights the limitations of MITICA, including the assumption that GHG emissions are solely influenced by proxies, which may overlook natural changes in emission profiles without public intervention. Future improvements could address this by incorporating varying levels of change in endogenous parameters. Additionally, the tool has not been empirically tested against alternative frameworks, and further research is needed to compare it with other models. MITICA's development aims to enhance transparency, consistency, and the ability of countries to track and report on their climate efforts, contributing to the global objectives of the Paris Agreement.