Rapid urbanization and population growth have created significant challenges in urban mobility management, such as traffic congestion, inefficient public transportation, and environmental pollution. This paper presents A framework for urban parking management and mobility forecasting, development and implementation of a Digital Twin (DT) aimed at addressing issues within the context of smart mobility. The framework integrates a wide range of historical and real-time data, including parking meter transactions, revenue records, street occupancy rates, parking violations, and sensor-based parking slot utilization. Additionally, the data encompass weather conditions, temporal patterns (such as weekdays and peak hours), and agent shift schedules, offering a comprehensive dataset for analyzing and optimizing urban mobility dynamics. Descriptive statistics are used to identify key patterns, while advanced Machine Learning (ML) and Deep Learning (DL) algorithms enhance predictive and generative analytics, forecasting parking demand and simulating various mobility scenarios. These insights, combined with visualization tools, map data onto the urban landscape, enabling spatial planning and resource allocation. Moreover, the integration of Generative Artificial Intelligence (GenAI) models significantly improves the system's capabilities, generating realistic ``what-if'' scenarios that allow for virtual testing of mobility strategies before real-world implementation. The results highlight the framework potential to improve urban mobility management, especially improving parking meter placement and enhancing the quality of urban mobility for users by reducing inefficiencies and improving accessibility. Tested on real-world data from the city of Caserta, the proposed framework has proven robust and adaptable, although expanding the dataset and refining specific components are necessary for fully realizing its potential and ensuring sustainable urban planning. Full Changelog: https://github.com/MODAL-UNINA/DTMOB/commits/v1.0.0