Visual impairment impacts approximately 2.2 billion individuals worldwide, underscoring the importance of effective communication between visually impaired and sighted people. Braille, a tactile system relying on dotted patterns, is crucial for visually impaired individuals. However, developing a universal Braille-to-voice conversion system poses challenges, especially for languages like Bengali, where such systems are underexplored. This study addresses this gap by introducing a deep neural network-based Bengali Braille voice transformation system tailored for Bengali-speaking visually impaired individuals. Our dataset, comprising 51,200 Braille images, is sourced from visual impairment schools (e.g., ShantiBiddlaya) and online repositories. We applied image preprocessing techniques—cropping, grayscale conversion, thresholding, dilation, erosion, segmentation, and augmentation—and evaluated them using PSNR, MSE, SSIM, and SNR metrics to ensure image quality. Six CNN models were employed for accurate Braille-to-voice conversion, including a customized CNN, VGG16, DenseNet201, AlexNet, Xception, and InceptionV3. Additionally, hybrid models combining CNNs with Random Forest (RF) and Support Vector Machine (SVM) enhanced performance. InceptionV3 achieved the highest accuracy (93.34%) among deep learning models, while the CNN-RF hybrid reached 95.22%. The CNN-SVM hybrid outperformed with a 96.57% accuracy, which increased to 98.93% through ablation studies. This proposed system provides an accurate method for converting Bengali Braille text to speech, fostering inclusive communication for visually impaired Bengali speakers and enhancing their engagement in everyday interactions.