This study examines risk interconnectedness and shock transmission across eight European stock markets over the period 2005–2025. It focuses on how market relationships change during financial and geopolitical crises and in response to European Central Bank (ECB) monetary policy. It also investigates whether dynamic spillover networks can improve forecasts of market connectedness. The empirical analysis combines econometric methods with a network-based deep learning approach. GARCH-family, VAR, and Markov-switching models are used to examine market volatility and shock transmission, while the Diebold–Yilmaz framework measures total and directional connectedness. An event study evaluates market reactions to ECB interest-rate decisions. The analysis covers major stress periods, including the Global Financial Crisis, the European sovereign debt crisis, Brexit, COVID-19, the Russia–Ukraine war, and the European energy crisis. The forecasting analysis uses a Graph Attention Network combined with a Gated Recurrent Unit (GAT–GRU) to predict future changes in the Total Connectedness Index (ΔTCI). The model combines dynamic spillover networks with market, ECB, and crisis information. Its performance is compared with simpler forecasting methods at 5-day and 22-day horizons, using historical volatility estimates and repeated tests with five random seeds. The results show a high level of financial connectedness across European stock markets, with a TCI of approximately 72.7%, indicating that a large share of market risk is linked to shocks originating in other markets. CAC 40 and DAX are the main net transmitters of shocks, while WIG20 and SOFIX are the main net receivers. Risk transmission is also stronger from euro-area markets to non-euro-area markets than in the opposite direction, while overall connectedness increases during major financial crises. Markets differ in their roles as shock transmitters and receivers, while their responses to ECB monetary policy are not uniform. Separate four-market models show much stronger internal connectedness in the euro-area group (static TCI: 69.69%) than in the non-euro-area group (41.35%), while the eight-market GFEVD results indicate stronger average cross-group spillovers from the euro area to the other markets. At both forecast horizons, GAT–GRU shows only small RMSE differences relative to Persistence, without consistent statistically significant improvements.