Financial time-series forecasting is difficult because of constantly changing market conditions, non-stationary behavior, and sudden changes in volatility which can negatively affect optimization stability during deep neural network training. Existing adaptive optimization methods such as Adam and its variants employ fixed optimization dynamics that do not directly respond to changing market conditions, which may lead to inefficient learning under varying volatility regimes. This study proposes VAdam, a Volatility-Aware Adaptive Gradient Optimization Framework for financial time-series learning. The proposed method builds on the conventional Adam optimizer by incorporating a volatility-conditioned update mechanism that dynamically adjusts learning rate and momentum behavior according to normalized market volatility. The method was evaluated on real cryptocurrency datasets obtained from Yahoo Finance, including Bitcoin, Ethereum, Solana, XRP, and Cardano, using a controlled optimizer-isolation benchmark with a shared MLP forecasting model. A comparison with SGD, RMSProp, Adam, AdamW, and Nadam demonstrates that VAdam achieves modest but consistent improvements over standard Adam, including 0.65% lower mean MSE, 0.67% lower price sMAPE, and 1.28% fewer epochs to convergence. The largest improvement was observed for XRP, where VAdam reduced MSE by 4.65% relative to Adam. The findings suggest that volatility-aware optimization can improve Adam-family optimization behavior while maintaining a simple drop-in optimization framework for financial forecasting tasks.