As Internet of Things (IoT) and IoT-Edge-Cloud continuum technologies advance, physical environments are becoming increasingly equipped with sensors, fuelling the development of smart space ecosystems. Massive quantities of data produced by IoT devices revolutionize the way such ecosystems operate via the exploitation of AI models/services. This has led to the emergence of the so-called Artificial Intelligence of Things (AIoT) systems. In general, designing techniques to promote robustness, efficiency and continual operation of AIoT systems requires realistic and trustworthy data at scale. However, such data is not always easy to obtain due to the cost of smart space construction, the inconvenience of long-term device tracking, the sensor/knowledge data gaps in diverse scenarios of a smart space, and the restrictions imposed on sensitive data sharing. Furthermore, an efficient AIoT system operation requires trustworthy AI services, as well as novel approaches for speeding up their inference across the IoT-Edge/Cloud continuum. PANDORA aims to devise and implement a comprehensive framework enabling the delivery of trustworthy datasets of smart space ecosystems, as well as the deployment and green operation of AIoT systems in such spaces. PANDORA spans two phases: (1) prior to AIoT system deployment; (2) post AIoT system deployment and operation. Phase 1 proposes and combines a series of novel techniques such as synthetic data generation, quantification of uncertainties, and data summarization for the delivery of trustworthy datasets, as well as explainable AI and domain-informed model training/testing in smart space ecosystems. Phase 2 defines novel AIaaS and CaaS techniques for the robust, explainable, green and continual operation of AIoT systems deployed in such spaces. The trustworthiness and applicability of the PANDORA framework will be tested through five pilot cases hosting AIoT applications in smart buildings, factories and critical infrastructures.