Traditional Feed-Forward Neural Networks (FFNN) and one-dimensional Convolutional Neural Networks (1D CNN) often encounter difficulties when dealing with long, columnar datasets that contain numerous features. The challenge arises from two primary factors: the large volume of data and the potential absence of meaningful relationships between features. In conventional training, large datasets can overwhelm the model, causing significant portions of the input to remain underutilized. As a result, the model may fail to capture the critical information necessary for effective learning, which leads to diminished performance. To overcome these limitations, we introduce a novel architecture called Parallel Multi-path Feed Forward Neural Networks (PMFFNN). Our approach leverages multiple parallel pathways to process distinct subsets of columns from the input dataset. By doing so, the architecture ensures that each subset of features receives focused attention, which is often neglected in traditional models. This approach maximizes the utilization of feature diversity, ensuring that no critical data sections are overlooked during training. Our architecture offers two key advantages. First, it allows for more effective handling of long, columnar data by distributing the learning task across parallel paths. Second, it reduces the complexity of the model by narrowing the feature scope in each path, which leads to faster training times and improved resource efficiency. The empirical results indicate that PMFFNN outperforms traditional FFNNs and 1D CNNs, providing an optimized solution for managing large-scale data.