The portfolio is a collection of assets belonging to an investor. Managing the portfolio depends on the goal of the portfolio management. This paper proposed a new portfolio managing technique using a deep reinforcement learning framework combined with meta-learning and signal bands to optimize the returns and risk of the Nifty 50 index. The objective is to maximize portfolio returns by minimizing the risk, portfolio volatility, and drawdowns with constraints of transaction cost, maximum and minimum allocation, and availability of cash and holdings. The model executes the actions of buy, sell, and hold with the constraints, and the model executes any of those actions depending on the situation and model training. Proposed model recorded a 4.68 Sharpe ratio and 7.53 Sortino ratio while training the model. While testing the model, it recorded a 4.5 Sharpe ratio and 7.64 Sortino ratio, which aligns with the aim to achieve a higher Sortino and Sharpe ratio to build a robust model for risk-adjusted returns. Proposed approach aims to create a strong model for a portfolio management system that adapts to dynamic market conditions and optimizes investment strategies by integrating these techniques.