Machine learning can also predict molecular bonding which is one of the most important steps in drug discovery. Some algorithms can predict whether a protein and ligament can be bonded by analyzing their chemical and structural properties. It can also predict the protein stability and the pattern of folding. The algorithm learns amino acid sequences to provide insight into their structure and function. Machine learning can also be used to predict proteinprotein interaction which is necessary to carry out various cellular functions. Machine learning algorithms analyze the datasets of proteinprotein interaction and predict the likelihood of a particular interaction which is primarily based on the DNA sequence, structure, and function. Algorithms also predict the interfaces between the antigen and antibodies, which is helpful in the manufacturing of vaccines and antibiotics. For example, the Support Learning Machine (SVM) algorithm predicts the binding affinity by finding a hyperplane that maximally separates the positive and negative training examples. Similarly, other algorithms such as Random Forest, Artificial Neural Network, and Molecular docking are also available which predict the 3D structure of proteins by docking of proteins. These models are trained by using experimental data collected from the literature studied by the scientists such as Darwin. Recently, ML algorithms have been devised in America to incorporate anaerobic digestion. Mechanistic models such as Anaerobic Digestion Model (ADM1) often cannot predict the nonlinear interactions between different process parameters such as Antibiotic resistance genes (ARG) and Mobile genetic elements (MGE). However, ML algorithms could offer insights into these interactions. Three widely used supervised algorithms namely Random forest (RF), XGBoost, and ANN are used to study the regression model of Anaerobic decay. These parameters are optimized by minimizing mean squared error. To predict the change in the ARG, and MGE each algorithm is devised such that it has input and output layers of neurons. During training, the loss function was set to be the mean squared error, and an optimizer called "ADAM" was used to minimize losses. Model performance was checked using the "Train TEST", in this test 80% data is used to train the model while remaining 20% is used to test whether the result is satisfactory. For further validation, many cross-tests were also performed by using holdout strategy. This draconian cross-validation approach ensured that only the model with the best generalization capability was selected. Similarly, ML can be used in the diagnosis of certain biological diseases. For example, a neurodegenerative disease, Alzheimer's, with no recognized cure or preventive measures, is indicated by Neurofibrillary tangles and amyloid plaques. However, various Al tools such as Arterial Spin Labelling-magnetic Resonance Imaging (ASL-MRI), Electroencephalograms (EEG) can timely identify it. ASL imaging identifies patterns of blood perfusion in different brain region., ASL perfusion maps can serve as potential biomarkers for Alzheimer's disease (AD) diagnosis at different stages of the disease. And by Integrating it with the MRI the growth of disease can be inhibited. The use of electroencephalography (EEG) provides valuable insights into typical brain function in Alzheimer's disease (AD) patients due to its superior temporal resolution. AD can impact the EEG in three ways: EEG slowing, warnings complexity reduction, and synchronization disruptions. Studies have shown that patients with mild cognitive impairment (MCI), a precursor to AD, exhibit higher energy levels in low frequency EEG bands such as delta and theta (0.5 Hz -8 Hz) and lower energy levels in higher frequency bands such as alpha and beta (8 Hz -30 Hz) when compared to healthy age-matched controls. These findings suggest that AD may affect specific frequency bands in the EEG in distinct ways.