Virtual Network Functions (VNFs) are essential components in modern networking that decouple network functions from dedicated hardware, thereby enhancing flexibility, scalability, and cost-efficiency. The inherently dynamic nature and fluctuating loads of networks, combined with evolving network demands, often lead to the over-allocation or underprovisioning of VNF resources, posing a significant challenge in optimal VNF resource usage. This issue becomes even more challenging when VNFs are connected in a specific sequence to fulfill a functional need-Service Function Chain (SFC). A promising solution to this challenge is proactive resource management-predicting resource demands of VNFs in advance. In this paper, we present a performance metrics prediction framework designed to anticipate multi-step resource demands for VNFs. The framework employs machine learning models, including RNN, LSTM, GRU, and Transformer models, within a robust meta-learner to capture complex usage patterns, accounting for both short-term and long-term dependencies in VNF resource consumption. This approach allows for precise prediction of critical key performance indicators (KPIs), including CPU usage, memory usage, processing latency, and traffic load for each VNF within an SFC. The evaluation results show that the proposed framework achieves a 75% reduction in mean absolute error (MAE) compared to the Transformer model and over 84% compared to RNN, LSTM, and GRU models. These results demonstrate the framework's substantial improvement over state-of-the-art approaches.