In our study, we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia (ALL). Built upon existing segmentation techniques, our approach enhances the dual threshold segmentation process, optimizing the isolation of nucleus and cytoplasm components. This is achieved by adapting threshold values based on image characteristics, resulting in superior segmentation outcomes compared to previous methods. To address challenges, such as noise and incomplete white blood cells, we employ mathematical morphology and median filtering techniques. These methods effectively denoise the images and remove incomplete cells, leading to cleaner and more precise segmentation. Additionally, we propose a unique feature extraction method using a hybrid discrete wavelet transform, capturing both spatial and frequency information. This allows for the extraction of highly discriminative features from segmented images, enhancing the reliability of classification. For classification purposes, we utilize an improved Adaptive Neuro-Fuzzy Inference System (ANFIS) that leverages the extracted features. Our enhanced classification algorithm surpasses traditional methods, ensuring accurate identification of acute lymphoblastic leukaemia. Our innovation lies in the comprehensive integration of segmentation techniques, advanced denoising methods, novel feature extraction, and improved classification. Through extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase (ALL-IDB) for Image Processing database using MATLAB 10.0, our method demonstrates outstanding classification accuracy. The segmentation accuracy for various cell types, including Band cells (96%), Metamyelocyte (99%), Myeloblast (96%), N. myelocyte (97%), N. promyelocyte (97%), and Neutrophil cells (98%), further underscores the potential of our approach as a high-quality tool for ALL diagnosis.