Cloud load forecasting predicts future demand on computing resources such as CPU, memory, and network band-width to facilitate proactive resource allocation, cost-effectiveness, and service-level conformance in cloud environments. This paper proposes an attention-driven spatiotemporal deep hybrid model for precise multivariate cloud load forecasting. The proposed architecture incorporates Convolutional Neural Networks (CNN) to extract short-term spatial and temporal trends, Long Short-Term Memory (LSTM) networks to capture long-range dependencies, and a soft attention mechanism to selectively highlight relevant timesteps in past observations. Hence, the proposed model, i.e., the Attention-enabled CNN-LSTM deep hybrid spatiotemporal model for Cloud load forecasting (ACL-Cloud), is trained on a publicly available cloud data center workload forecast dataset in an end-to-end pipeline. Input sequences are built utilizing a sliding window strategy over five normalized resource measures: CPU usage, memory consumption, memory capacity provisioned, and network transmitted and received throughput. Comprehensive experimental results and analysis show that the proposed ACL-Cloud model exhibits better accuracy and generalization in cloud load forecasting tasks, enabling its potential deployment in an intelligent and scalable cloud infrastructure management framework.