Cell-free massive MIMO (CF-mMIMO) combined with integrated sensing and communication (ISAC) is a promising architecture for future 6G networks, enabling new sensing-based applications. However, integrating sensing functionality increases power consumption across the radio, fronthaul, and cloud domains, which is not captured by conventional transmit power optimization approaches. In this paper, we develop a cross-layer end-to-end (E2E) optimization framework for green CF-mMIMO ISAC systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics and forward them to the cloud for aggregation via a weighted combination strategy. We derive maximum a posteriori ratio test (MAPRT) detectors under fully informed (FIS) and partially informed (PIS) scenarios, capturing different levels of side information available at the RX-APs. We formulate a joint optimization problem that minimizes total network power consumption by jointly optimizing transmit power allocation, AP operation modes, communication user and sensing associations, RX-AP assignments, and cloud/fronthaul resources, subject to communication and sensing constraints. The resulting mixed-integer non-convex problem is solved via a two-stage iterative algorithm based on successive convex approximation and penalty-based relaxation. Numerical results demonstrate that the proposed E2E framework significantly reduces total power consumption compared to benchmark schemes, achieving more than 50% savings over transmit-power-only optimization and approximately 13-15% over radio optimization, while maintaining competitive detection performance.

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