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This paper proposes a hybrid framework\u00a0that integrates Long Short-Term Memory (LSTM) networks for temporal workload forecasting with Elephant Herding Optimization with Memory (EHOM) for multi-objective task-to-host allocation. The framework is implemented as containerized microservices on an OpenStack Yoga private cloud with Prometheus telemetry, Kafka streaming, TensorFlow Serving for LSTM-based forecasting, and a Python-based EHOM optimizer orchestrated through OpenStack. Performance is benchmarked against Trust-Aware Spring Swarm Optimization (TSSO), Auto Clipped Double Deep Q-Learning (Auto-CDDQL), Self-Adaptive Flower Pollination-based RSA (SA-FPRSA), Elephant Herding Lion Optimizer (EHLO), and a Markov-based scheduler. Experimental results across workloads of 200\u20131000 tasks show that the proposed method reduces total energy consumption by 12\u201322%, decreases normalized delay by 8\u201315%, and improves deadline satisfaction ratio (DSR) by 2.5\u20135.3 percentage oints, while consistently maintaining availability and reliability above 97%. These improvements confirm the robustness, scalability, and real-time applicability of the proposed framework for SLA-sensitive multi-cloud environments. 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