Ensuring uninterrupted service delivery in large-scale cloud infrastructures remains a persistent challenge due to the stochastic nature of hardware faults, software anomalies, and workload volatility. Despite significant advances in virtualization and distributed orchestration, most operational platforms continue to rely on reactive fault management strategies that initiate recovery only after service degradation becomes observable, thereby prolonging downtime and increasing operational overhead. This limitation underscores the necessity for predictive and autonomous resilience mechanisms capable of identifying incipient failures before they propagate across interconnected cloud components. This study proposes an intelligent self-healing cloud framework that integrates deep learning-based failure prediction with automated recovery orchestration. The proposed system continuously monitors multidimensional telemetry streams, including CPU utilization, memory consumption, network latency, and disk I/O patterns, and models temporal dependencies using a Long Short-Term Memory (LSTM) architecture trained on large-scale operational traces derived from publicly available datasets such as the Google Cluster Trace and Alibaba Cloud cluster logs. The predictive module estimates the conditional probability of system failure given observed system states, formally expressed as $P(F_t \mid X_t) = \sigma(WX_t + b)$, where $X_t$ represents the vector of runtime metrics at time $t$, $W$ denotes learned parameters, and $\sigma(\cdot)$ is the nonlinear activation function governing classification confidence. Upon exceeding a predefined reliability threshold, the framework autonomously triggers corrective actions, including container restart, virtual machine migration, and dynamic resource scaling within a Kubernetes-based experimental environment. Extensive simulation results demonstrate measurable improvements in service availability and recovery latency, with the proposed mechanism reducing mean time to recovery (MTTR) and minimizing service disruption under varying workload intensities. These findings indicate that predictive intelligence combined with automated remediation can substantially enhance operational resilience in modern cloud ecosystems. The principal contribution of this work lies in the design and empirical validation of an autonomous cloud resilience framework that unifies deep learning-driven failure anticipation with real-time self-healing control for dependable large-scale cloud services.

Keywords: Cloud Computing, Self-Healing Systems, Deep Learning, Failure Prediction, Fault Tolerance, Cloud Reliability, Autonomous Systems, AIOps