Emerging AI-driven pervasive health and wellbeing (PHW) services (e.g., personalized health assistants and mobile health applications) face critical challenges in handling noisy/intermittent sensory data, integrating cross-modal insights, and stringent energy and compute constraints. We present Mindful AI, a cognitive-inspired framework designed to enable adaptive, resilient, and efficient PHW services in real-world conditions. Our dual-mode intelligence—Automatic (System 1) and Reflective (System 2)—selectively directs system attention toward the most relevant sensing and compute contexts, unifying bottom-up stimuli (driven by input quality, inference demands and model confidence, and resource availability) with top-down insights (reflecting user demands, system goals/constraints, and contextual information). Our framework distills and orchestrates insights across sensing, communication, and computation through hybrid attention toward bottom-up and top-down insights that support cross-layer sense-compute co-optimization to achieve resilient, low-latency, and energy-efficient PHW services. We evaluate our approach on multi-tier device-edge-cloud platforms, using real-world case studies in pain assessment, stress monitoring, and human activity recognition to demonstrate adaptation to real-world uncertainties (e.g., sensor degradation, context drift, network variability), while maintaining strict QoS, accuracy, and latency guarantees.