In today’s fast-paced digital environment, delays due to cold start can drastically impact cloud computing, edge systems, and smart manufacturing. This study introduces ColdStartNet, a strong hybrid architecture that predicts cold start latency minutes ahead of benchmark conditions so that systems can be proactive to avoid impacting performance. ColdStartNet consists of a stack of machine learning and deep learning models, including: Random Forest, XGBoost, LightGBM, ARIMA, and LSTM, taking advantage of XGBoost as a meta-learner within the stack. To assist the models in leaning from previous behaviors, we formed features from time-related factors (Hour, Day, Minute), CPU-memory interaction, and historical latency changes and variation. After careful tuning and optimization of hyperparameters for each of the constituent models, ColdStartNet performed exceptionally well reducing error for time-latency predictions down to 5.97 ms (MAE) and providing great consistency (R2 Score: 0.83). This predictive capacity will help businesses and cloud service providers facilitate resource allocation, decrease downtime, and improve overall user experience by planning for cold start latency. Due to its flexibility, ColdStartNet can be used for cloud computing, IoT ecosystems, and high-performance computing.