## 1 Introduction
These models often prioritize either latency or throughput independently and fail to adjust to real-time network fluctuations or service priority shifts. Such rigidity leads to service degradation under load surges, network failures, or emergency conditions. Most existing service provisioning approaches in edge-cloud IoT ecosystems suffer from three key limitations: (i) they treat latency and service delivery rate independently, optimizing one at the expense of the other rather than addressing both jointly for QoS-critical scenarios; (ii) they rely on static or heuristically tuned allocation models that lack adaptability under dynamic and bursty healthcare traffic [10; 11]; and (iii) they overlook predictive learning and reinforcement-based control, which are essential for proactive decision-making in time-sensitive and resource-constrained environments. As illustrated in Figure 1, a dynamic mist-edge-cloud strategy is needed that adaptively chooses optimal execution paths based on predicted traffic, latency sensitivity, and available capacity. This motivates the need for ML-RASPF: a unified learning framework that jointly optimizes latency and delivery rate under realistic smart healthcare conditions. Recent rate-adaptive methods such as those by [PERSON] et al. [9], [PERSON] et al. [8], and [PERSON] et al. [7] have introduced optimization-based or queue-aware scheduling for IoT service allocation. However, these approaches often target latency-tolerant services and fall short in supporting real-time use cases such as ECG monitoring, emergency response, or clinical decision support, where service disruptions or delays can be life-threatening. Moreover, these methods do not jointly model latency and delivery rate in a unified framework, leading to inefficiencies during network surges or unexpected traffic spikes. Furthermore, many existing solutions impose continuous load monitoring or centralized scheduling overheads that do not scale effectively in distributed mist-edge-cloud environments, especially where edge nodes are resource-limited [12; 13]. The lack of modular, learning-enhanced strategies also makes it difficult to adapt to dynamic service demands in real time without incurring excessive computation or communication costs. These shortcomings present a need for a unified and lightweight ML framework that can predict traffic variation, dynamically adapt service rates, and allocate resources across hierarchical layers with minimal latency. An intelligent provisioning system that jointly optimizes latency and service delivery rate while maintaining scalability and robustness under constrained settings remains an open research challenge in smart healthcare IoT. To address these limitations, we present ML-RASPF, an ML-based framework designed to enable intelligent, rate-adaptive, and latency-aware service provisioning in smart healthcare environments. As illustrated in Figure 1, ML-RASPF operates across mist, edge, and cloud layers, dynamically adjusting service paths and resource allocation based on real-time service demands and network conditions. The framework integrates supervised learning (LSTM) for traffic forecasting and reinforcement learning (RL) for rate adaptation, enabling predictive, context-aware decisions across the hierarchy. Edge nodes collaborate vertically to ensure that both latency-sensitive and throughput-heavy services meet their respective QoS constraints. This work proposes and evaluates a unified service provisioning framework that jointly optimizes latency and delivery rate, addressing critical gaps in adaptability, predictive control, and real-time responsiveness across heterogeneous IoT workloads. ML-RASPF delivers robust performance under dynamic loads while minimizing energy and bandwidth usage. Extensive simulations in a realistic smart hospital scenario validate its superiority over existing approaches.