Accurate forecasting of day level parcel count is crucial in the logistics industry as it is directly linked to staffing systems help in resource planning. However, it is challenging to forecast over extended periods due to data variability and the unpredictability of future events, often leading to reduced model accuracy. Although classical Long-Short-Term-Memory (LSTM) models, are effective for time series forecasting, they encounter challenges due to computational complexity and large trainable parameters. To address these limitations, we propose a Quantum Enhanced Long-Short-Term-Memory (QELSTM), a deep learning-based approach to solve the time series forecasting problem of one of the largest logistics companies in Europe. Leveraging quantum properties such as superposition and entanglement, QELSTM achieves \(\sim 3\%\) better accuracy than classical LSTM, with \(\sim 3.78\) times fewer trainable parameters and \(\sim 3.66\) times smaller number of computations during training. QELSTM’s constant circuit depth enhances scalability for Noisy Intermediate-Scale Quantum (NISQ) computers, unlike existing Quantum Long-Short-Term-Memory (QLSTM) models, which exhibit increasing circuit depths with qubit count. The proposed model is also validated on Apple (AAPL) daily stock price data, showing faster convergence than existing QLSTMs and LSTM with comparable accuracy.