Integrated sensing and communication (ISAC) has emerged as a key cross-domain technology in sixth-generation (6G) networks. To overcome the limitations of single-view ISAC systems, the MICCAS-6G project aims to develop an innovative multi-view intelligent collaborative communication and sensing system. By leveraging advanced synchronization and calibration methods, distributed signal processing, and artificial intelligence (AI), MICCAS-6G seeks to enable distributed multi-view sensing through full coordination of users and base stations. This will improve the accuracy, robustness, and deployability of ISAC in complex scenarios. Despite its potential, the MICCAS system faces key challenges. First, achieving precise time and phase alignment across multiple distributed nodes is difficult but critical for collaborative multi-view sensing. Second, data heterogeneity arises from varied sensing views and partial coverage, making data fusion challenging. Third, large-scale networks introduce coordination overhead, demanding efficient inter-node strategies. Lastly, high-dimensional data in large-scale scenarios requires scalable processing techniques. The project will develop an AI-driven method to process high-dimensional, distributed multi-view ISAC data with reduced processing delay and improved adaptability in large-scale wireless networks. First, the project will apply multi-view learning techniques to handle observation heterogeneity across distributed nodes. By learning shared representations from partially aligned or inconsistent views, the model can effectively integrate complementary information from multiple sources, for example using GNNs or variational autoencoder-based methods. This enables reliable fusion even under partial coverage, occlusion, or variation in sensing quality. Second, to handle the computational and communication burden of high-dimensional data, the project will implement lightweight distributed inference mechanisms. These include compressed channel representations and feature- level abstraction to reduce inter-node data exchange. GNNs and federated learning techniques will be considered for integration to support scalable optimization and model adaptation across heterogeneous nodes without centralized retraining. In combination, these methods will enable scalable, adaptive, and resource- efficient multi-view sensing capabilities, aligning with the real-time requirements and deployment constraints of future 6G ISAC systems. Expected outcomes include novel synchronization schemes, low-complexity distributed algorithms, and robust AI-based fusion methods. These results will support ISAC deployment in areas like autonomous driving and smart cities, and foster advancements in both academia and industry.