Sampling-based path planning algorithms play an important role in autonomous robotics. However, a common problem among these algorithms is that the initial path generated is not optimal, and the convergence is too slow for real-world applications. In this paper, we propose a novel image-based learning algorithm using a Convolutional Block Attention Generative Adversarial Network (CBAGAN-RRT) with a combination of spatial and channel attention and a novel loss function to design the heuristics, find a better optimal path, and improve the convergence of the algorithm, both concerning time and speed. The probability distribution of the paths generated from our GAN model is used to guide the sampling process for the RRT algorithm. We demonstrate that our algorithm outperforms the previous state-of-the-art algorithms using both the image quality generation metrics, like IOU Score, Dice Score, FID score, and path planning metrics like time cost and the number of nodes. Ablation studies show the effectiveness of various components in our network architecture. The advantage of our approach is that we can avoid the complicated preprocessing in the state space, our model can be generalized to complex environments like those containing turns and narrow passages without loss of accuracy, and our model can be easily integrated with other sampling-based path planning algorithms.